Transforming real-world, real time body movements, captured via camera, into dynamic Super Smash Bros. gameplay on Nintendo 3DS. Gesture recognition enables players to physically execute fighting moves, providing an immersive and engaging experience. Calibration ensures accuracy, while multiplayer interactions enhance excitement. Emphasis on accessibility and customization for a diverse gaming audience.
- Exemplifies the use of gesture recognition to translate real-world movements into in-game actions.
- Demonstrates the potential of motion control to enhance accessibility in gaming.
- Illustrates the application of motion control to create an engaging dance experience.
- Emphasizes the significance of gesture recognition accuracy and timing.
- Highlights the potential of virtual reality to create immersive and interactive gaming experiences.
- Demonstrates the use of motion control to elevate the sense of presence in VR games.
Your project involves creating an innovative and immersive gaming experience for Super Smash Bros. on the Nintendo 3DS. The core concept is to leverage a computer camera to capture real-world body movements and translate them into in-game actions. Here's an overview:
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Objective:
- Create a unique and interactive gameplay experience where players can control in-game characters by physically moving their bodies.
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Components:
- Camera Integration: Utilize a computer camera to capture real-world body movements.
- Emulator Setup: Employ a Nintendo 3DS emulator on your computer for testing and gameplay simulation.
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Technical Steps:
- Camera Calibration: Implement a calibration system for precise motion tracking.
- Motion Tracking: Develop computer vision algorithms to track different body parts in real-time.
- Gesture Recognition: Create a sophisticated system to recognize gestures based on tracked movements.
- Mapping Gestures to Game Actions: Associate recognized gestures with specific in-game moves in Super Smash Bros.
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Emulator Control:
- Emulator Integration: Programatically control the 3DS emulator to reflect the recognized gestures.
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Realism and Gameplay:
- Realistic Fighting Moves: Fine-tune the system to ensure in-game characters mimic the player's real-world movements.
- Dynamic Gameplay: Implement features that respond dynamically to various gestures, adding depth and strategy to the gameplay.
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User Interaction:
- Calibration and Precision: Provide options for users to calibrate the system for their specific playing environment.
- Customization: Include settings for players to adapt the system to their preferences and physical abilities.
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Testing and Iteration:
- Conduct thorough testing to identify and resolve issues related to gesture recognition, mapping, and overall gameplay.
- Iterate based on user feedback and testing results to enhance the user experience.
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Documentation and User Guide:
- Create comprehensive documentation detailing setup, calibration, customization, and troubleshooting steps.
- Develop a user guide to assist players in using and optimizing the system.
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Optional Features:
- Graphical User Interface (GUI): Consider implementing a user-friendly interface for easier interaction.
- Multiplayer Interaction: Explore possibilities for multiplayer interactions, enhancing the social aspect of the gaming experience.
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Ethical Considerations:
- Consider and address ethical implications, ensuring user privacy and adhering to ethical gaming practices.
Camera Integration:
- Choose a suitable computer camera to capture real-world body movements.
- Set up and configure the camera for use in a Python environment.
Emulator Setup:
- Select and set up a Nintendo 3DS emulator on your computer.
- Ensure the emulator supports external input or control customization.
Camera Calibration:
- Implement a camera calibration process to ensure accurate motion tracking.
- Develop a calibration tool that allows users to set up and optimize camera parameters for their specific environment.
Motion Tracking:
- Utilize computer vision techniques to track the player's body movements in real-time.
- Implement algorithms to recognize different body parts and their positions using the captured camera data.
Gesture Recognition:
- Develop a sophisticated gesture recognition system based on the tracked body movements.
- Identify key gestures relevant to Super Smash Bros. gameplay, such as punches, kicks, and jumps.
Mapping Gestures to Game Actions:
- Create a mapping system that associates recognized gestures with specific in-game actions in Super Smash Bros.
- Consider how different types and intensities of movements translate to various actions in the game.
Emulator Control Integration:
- Explore the emulator's documentation to understand how to programmatically control input.
- Implement code to send commands to the 3DS emulator based on the recognized gestures.
Realistic Fighting Moves:
- Fine-tune the mapping system to ensure that in-game characters mimic the player's real-world movements realistically.
- Test and iterate on the mapping to achieve a balance between realism and gameplay responsiveness.
Calibration and Precision:
- Implement options for users to calibrate the system for their specific playing environment.
- Fine-tune calibration parameters to achieve high precision in translating real-world movements to in-game actions.
Dynamic Gameplay:
- Design and implement dynamic gameplay features that respond to the player's physical movements.
- Consider how different gestures and combinations of movements contribute to diverse in-game actions.
Accessibility Features:
- Include customization options to accommodate players with varying physical abilities.
- Ensure that the system remains adaptable and user-friendly for a diverse audience.
Testing and Iteration:
- Conduct extensive testing to identify and resolve any issues related to gesture recognition, mapping, and overall gameplay.
- Iterate on the project based on user feedback and testing results.
Documentation and User Guide:
- Create comprehensive documentation outlining how users can set up and use the system.
- Provide a user guide that explains calibration, customization, and troubleshooting steps.
Optional Features:
- Graphical User Interface (GUI): Consider implementing a user-friendly interface (GUI) for users to interact with calibration and customization settings more intuitively.
- Multiplayer Interaction: Explore possibilities for multiplayer interactions, enhancing the social aspect of the gaming experience.
Ethical Considerations:
- Consider the ethical implications of your system, ensuring that it respects user privacy and adheres to ethical gaming practices.
In summary, your project involves integrating computer vision, gesture recognition, and emulator control to enable players to control Super Smash Bros. characters through real-world body movements. It aims to provide an immersive and accessible gaming experience, offering a new level of interaction and enjoyment for players.
Research: Certainly! Exploring motion tracking datasets is a crucial step in developing and evaluating models for tasks like human pose estimation and motion analysis. Here's a guide on some widely used datasets:
- Human3.6M: Overview:
Link: Human3.6M Dataset Human3.6M is a large-scale dataset for human motion capture, containing 3.6 million RGB images. It provides 3D joint positions, ground truth 2D poses, and videos captured in controlled environments. Usage:
Ideal for research on 3D pose estimation, action recognition, and motion analysis. Commonly used for evaluating the performance of deep learning models on complex human activities. 2. CMU Motion Capture Database: Overview:
Link: CMU Motion Capture Database CMU Motion Capture Database is a collection of motion capture sequences featuring a variety of activities. It includes both indoor and outdoor motion capture data. Usage:
Widely used for research in animation, biomechanics, and computer vision. Suitable for tasks like motion segmentation, activity recognition, and gesture analysis. 3. MPI-INF-3DHP: MPI-INF-3D Human Pose Dataset: Overview:
Link: MPI-INF-3DHP Dataset MPI-INF-3DHP is a dataset for 3D human pose estimation. It provides synchronized multi-camera data, 3D joint positions, and detailed annotations for challenging poses. Usage:
Useful for developing and evaluating 3D human pose estimation models. Offers realistic scenarios for assessing the robustness of algorithms in challenging conditions. Tips for Exploration: Data Format:
Understand the format in which the data is provided. Datasets often include RGB images, depth maps, and annotations for joint positions. Annotation Details:
Examine the annotation details. Look for labeled keypoints, skeleton structures, and any additional information provided by the dataset. Sample Visualizations:
Visualize samples from the dataset to get a sense of the data distribution and the complexity of motion patterns. Baseline Models:
Explore existing literature to find baseline models or benchmarks established using these datasets. This can provide insights into the state-of-the-art approaches. Data Preprocessing:
Depending on your task, consider the necessary preprocessing steps. This may involve normalization, resizing, or data augmentation to enhance model generalization. Research Papers:
Look for research papers or publications associated with the datasets. These papers often provide detailed information about the dataset characteristics and challenges. Code Repositories:
Explore code repositories associated with the datasets. Researchers often share code for data loading, preprocessing, and model evaluation. Community Forums:
Participate in relevant community forums or discussion groups. Engaging with the community can provide valuable insights and assistance when working with specific datasets. By familiarizing yourself with these motion tracking datasets, you gain a solid foundation for experimenting with different deep learning models and techniques in the domains of human pose estimation and motion analysis.
Best one to consider so far is MPI-INF-3DHP Dataset
Will likely be using Convolutional Neural Networks (CNNs) cause images and in future Recurrent Neural Networks(RNNs) to keep track of past inputs like NLP
Convolutional Neural Networks (CNNs): Purpose:
CNNs are specialized neural networks designed for image-related tasks, such as image recognition and computer vision. Convolutional Layers:
Convolutional Operation: Applies filters to input data to extract features. Pooling Layers: Reduce spatial dimensions, preserving important information. Feature Learning:
CNNs automatically learn hierarchical features from input data. Recurrent Neural Networks (RNNs): Sequential Data:
RNNs are suitable for processing sequential data, like time series or natural language. Hidden State:
RNNs maintain a hidden state that captures information about previous inputs in the sequence. Long-Term Dependencies:
Despite advantages, traditional RNNs struggle with capturing long-term dependencies. Long Short-Term Memory (LSTM) Networks: Addressing Shortcomings:
LSTMs are a type of RNN designed to overcome the challenge of capturing long-term dependencies. Cell State:
LSTMs introduce a cell state that runs parallel to the hidden state, allowing information to be added or removed. Gates:
Forget Gate: Controls what information from the cell state should be discarded. Input Gate: Updates the cell state with new information. Output Gate: Produces the final output based on the cell state.
Remember these are baseline's there are more advanced models and techniques based off these that may better suite me
Remember to ask chatgpt about challenges I would likely face: Eg occlusion
Tackle real-world challenges: Address real-world challenges in motion tracking, such as occlusions, lighting conditions, and real-time performance. Consider techniques like predictive tracking, multi-view tracking, part-based tracking, adaptive thresholding, background subtraction, illumination normalization, model simplification, feature reduction, and efficient inference algorithms.
Contribute to the open-source community: Engage with the open-source community by contributing to projects like OpenPose, DeepPose, and PyKinect for Windows. This will allow you to learn from experienced developers, contribute to the advancement of motion tracking, and gain valuable insights into real-world applications.
Stay up-to-date with research: Continuously monitor the latest advancements in deep learning-based motion tracking by reading research papers, attending conferences, and participating in online discussions. This will keep you abreast of cutting-edge techniques and emerging trends in the field.
rewrite the above it's readable and reduce redundance and reorganise structure if necessary. also retitle sections if necessary
I'm scraping use of MPI-INF-3DHP because it cant be used in real time
best human pose features to extract to train an ai to recognize specific fighting poses View other drafts
Enhanced Project Goal with Mediapipe:
"Develop a real-time fighting action recognition system that utilizes Mediapipe for pose estimation to extract landmark features from input frames captured by a camera. The system should accurately classify and identify various fighting actions, including low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. The focus is on creating a robust and efficient solution capable of real-time action recognition within a fighting context, leveraging the pose estimation capabilities of the Mediapipe library."
For feature extraction in your real-time fighting action recognition system, you want to capture relevant information from the pose landmarks provided by Mediapipe. The choice of features is crucial for effectively distinguishing between different fighting actions. Here are some potential features you can consider:
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Joint Angles:
- Calculate the angles between key joints to capture the orientation of limbs during different actions. For example, the angle between the hip, knee, and ankle joints can provide information about the leg's position during kicks.
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Relative Joint Positions:
- Express the positions of specific joints relative to others. For instance, the relative position of the hand with respect to the head might be informative for actions like dodges.
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Trajectory of Joints:
- Analyze the trajectory of specific joints over time to capture dynamic aspects of actions. Tracking the movement of the hands or feet can be useful for recognizing punching, kicking, or dodging motions.
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Temporal Features:
- Consider including temporal features to capture the sequence of movements. This can involve analyzing the velocity or acceleration of specific joints over consecutive frames.
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Body Part Interaction:
- Explore interactions between different body parts. For example, recognizing when the hand interacts with the leg during a kick or identifying instances where hands are near the face during a dodge.
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Stance and Balance:
- Assess the balance and stance of the person by analyzing the distribution of weight across different joints. A well-balanced stance might indicate preparation for a kick or punch.
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Hand Gesture Features (if applicable):
- If your fighting actions involve specific hand gestures, extract features related to hand positions, angles, or interactions. Hand features could be crucial for recognizing actions like blocking or grabbing.
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Symmetry and Asymmetry:
- Analyze the symmetry or asymmetry of the pose. Certain fighting actions might exhibit distinct asymmetries, and detecting these can aid in action recognition.
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Customized Landmark Distances:
- Define and calculate specific distances between relevant landmarks. For example, the distance between the hands during a punch or the distance between the foot and head during a kick.
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Pose Dynamics:
- Capture the dynamics of the overall pose, including changes in body orientation and shape over time.
It's essential to experiment with different combinations of these features and assess their effectiveness in distinguishing between various fighting actions. Additionally, consider the input representation format (e.g., a sequence of feature vectors) that will be fed into your action recognition model. The success of your system will depend on the ability of these features to capture discriminative information for each action class.
Robustness to Noise:
Ensure that your selected features are robust to variations in lighting conditions, camera angles, and other potential sources of noise in real-world scenarios
For your goal of recognizing fighting actions based on pose estimation using Mediapipe, you can consider a variety of action recognition models. The choice depends on factors such as model complexity, real-time performance, and the size of your dataset. Here are some popular action recognition models that you may find suitable:
Analyse which of between TSM or SlowFast Networks will help me best for this project goal ""Develop a real-time fighting action recognition system that utilizes Mediapipe for pose estimation to extract landmark features from input frames captured by a camera. The system should accurately classify and identify various fighting actions, including low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. The focus is on creating a robust and efficient solution capable of real-time action recognition within a fighting context, leveraging the pose estimation capabilities of the Mediapipe library." View other drafts
Based on your project goal, SlowFast Networks would likely be a better choice than TSM for several reasons:
Real-time Performance:
SlowFast Networks are specifically designed for efficient processing, making them well-suited for real-time applications like yours. This is critical for your fighting action recognition system, as it needs to respond to actions instantaneously for effective training and analysis. TSM, while efficient compared to other 3D convolutional networks, may still struggle to achieve real-time performance on moderate hardware. Accuracy for Diverse Fighting Actions:
Your project aims to recognize a variety of fighting actions, including simple kicks and more complex maneuvers like dodges. SlowFast Networks excel at handling both spatial and temporal features, allowing them to capture subtle differences in movement patterns crucial for differentiating these diverse actions. While TSM offers good accuracy for simpler actions, it might struggle with recognizing nuanced movements due to its focus on fine-grained temporal modeling. Hardware Compatibility:
You mention using a typical home Lenovo computer, which likely has a moderate GPU suitable for running models efficiently. SlowFast Networks are designed to run well on such hardware, making them a more practical choice than TSM, which might require more powerful resources. Scalability and Future Needs:
SlowFast Networks offer greater flexibility and scalability compared to TSM. This is beneficial for future expansion of your project, as your dataset and complexity of actions might increase. SlowFast Networks can be easily adapted to handle larger datasets and additional action classes without significant changes to the overall architecture. Additional factors in favor of SlowFast Networks:
Pre-trained models: Several pre-trained SlowFast models are readily available, significantly reducing training time and improving performance. Optimization techniques: Various optimization techniques like quantization and pruning can be applied to further enhance the efficiency of SlowFast Networks on your specific hardware. Community support: SlowFast Networks have a larger and more active community compared to TSM, providing easier access to resources and troubleshooting support. Conclusion:
While TSM might offer slightly higher accuracy for specific tasks, its efficiency limitations and potential compatibility issues make it less suitable for your real-time fighting action recognition system. SlowFast Networks provide a more balanced solution between accuracy, efficiency, and real-time performance, making them the recommended choice for your project goals.
Remember, experimenting with both methods and evaluating their performance on your specific dataset is always recommended for optimal results.
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Two-Stream Networks:
- Description: These networks have separate spatial and temporal streams, making them suitable for processing static pose information and capturing temporal dynamics.
- Implementation: You can design a two-stream network, with one stream handling the spatial pose features and the other capturing temporal information.
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I3D (Inflated 3D ConvNet):
- Description: I3D is designed for video action recognition by inflating 2D models to 3D. It's effective in capturing temporal features in a sequence of frames.
- Implementation: Adapt I3D to handle the extracted pose features over time for action recognition.
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C3D (Convolutional 3D):
- Description: C3D is another 3D convolutional neural network suitable for processing video data and capturing temporal information.
- Implementation: Adapt C3D for recognizing fighting actions based on the temporal evolution of pose data.
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TRN (Temporal Relational Network):
- Description: TRN is designed to capture long-range dependencies in temporal sequences. It's suitable for recognizing actions in video data.
- Implementation: Integrate TRN to analyze the temporal relationships within the sequence of pose information.
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TSM (Temporal Shift Module):
- Description: TSM introduces temporal shifts to capture temporal information effectively. It is known for its efficiency in action recognition tasks.
- Implementation: Incorporate TSM into your model architecture for improved temporal modeling.
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SlowFast Networks:
- Description: SlowFast networks consist of both slow and fast pathways, allowing for efficient processing of both spatial and temporal features.
- Implementation: Configure a SlowFast network to handle the static and dynamic aspects of pose information.
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LSTM (Long Short-Term Memory):
- Description: LSTM is a type of recurrent neural network suitable for capturing sequential dependencies in data.
- Implementation: Use LSTM to process the temporal sequence of pose features and recognize actions.
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3D ResNets:
- Description: 3D ResNets are variants of the popular ResNet architecture adapted for video-based action recognition.
- Implementation: Utilize 3D ResNets for recognizing fighting actions based on the sequence of pose features.
When implementing these models, consider the specific requirements of your real-time system, the size of your dataset, and the complexity of the fighting actions you want to recognize. You may need to experiment with different architectures and fine-tune them to achieve optimal performance for your specific use case.
Real-time action recognition requires models that can process input data quickly and efficiently. Here are some models and approaches known for their real-time performance:
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MobileNetV2:
- Description: MobileNetV2 is a lightweight convolutional neural network designed for mobile and edge devices. It's known for its efficiency and is suitable for real-time applications.
- Implementation: You can modify or design an architecture that integrates MobileNetV2 for action recognition based on pose features.
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EfficientNet:
- Description: EfficientNet is another efficient neural network architecture that balances model size and performance. It can be adapted for real-time action recognition tasks.
- Implementation: Experiment with EfficientNet or its variants for real-time processing of pose features.
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Real-time Multi-Person Pose Estimation (RMPE) + Lightweight Action Model:
- Description: Use a lightweight action recognition model in combination with a real-time multi-person pose estimation model like RMPE.
- Implementation: Apply RMPE for pose estimation, extract features, and feed them into a compact action recognition model suitable for real-time processing.
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Online Real-Time Action Detection (ORTAD):
- Description: ORTAD is specifically designed for online real-time action detection. It considers both spatial and temporal information efficiently.
- Implementation: Explore the ORTAD architecture for action recognition based on real-time pose data.
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Single Shot MultiBox Detector (SSD):
- Description: SSD is an object detection model that can be adapted for action recognition. It's known for its real-time performance.
- Implementation: Modify SSD to focus on recognizing actions based on pose features in real-time.
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Single-Frame Action Recognition Models:
- Description: Some models are designed for single-frame action recognition, suitable for real-time processing. These models often consider the spatial features of a single frame.
- Implementation: Explore and adapt single-frame action recognition models to your specific use case.
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Temporal Shift Module (TSM):
- Description: TSM introduces temporal shifts to efficiently capture temporal information. It has shown effectiveness in real-time action recognition.
- Implementation: Incorporate TSM into your model architecture for real-time processing of pose features.
When implementing these models, pay attention to model size, computational efficiency, and the ability to handle the real-time constraints of your application. Additionally, consider optimizations such as model quantization and hardware acceleration to further enhance real-time performance. Experimenting with different models and techniques will help you find the best balance between accuracy and speed for your specific use case.
Real-time action recognition requires models that can process input data quickly and efficiently. Here are some models and approaches known for their real-time performance:
-
MobileNetV2:
- Description: MobileNetV2 is a lightweight convolutional neural network designed for mobile and edge devices. It's known for its efficiency and is suitable for real-time applications.
- Implementation: You can modify or design an architecture that integrates MobileNetV2 for action recognition based on pose features.
-
EfficientNet:
- Description: EfficientNet is another efficient neural network architecture that balances model size and performance. It can be adapted for real-time action recognition tasks.
- Implementation: Experiment with EfficientNet or its variants for real-time processing of pose features.
-
Real-time Multi-Person Pose Estimation (RMPE) + Lightweight Action Model:
- Description: Use a lightweight action recognition model in combination with a real-time multi-person pose estimation model like RMPE.
- Implementation: Apply RMPE for pose estimation, extract features, and feed them into a compact action recognition model suitable for real-time processing.
-
Online Real-Time Action Detection (ORTAD):
- Description: ORTAD is specifically designed for online real-time action detection. It considers both spatial and temporal information efficiently.
- Implementation: Explore the ORTAD architecture for action recognition based on real-time pose data.
-
Single Shot MultiBox Detector (SSD):
- Description: SSD is an object detection model that can be adapted for action recognition. It's known for its real-time performance.
- Implementation: Modify SSD to focus on recognizing actions based on pose features in real-time.
-
Single-Frame Action Recognition Models:
- Description: Some models are designed for single-frame action recognition, suitable for real-time processing. These models often consider the spatial features of a single frame.
- Implementation: Explore and adapt single-frame action recognition models to your specific use case.
-
Temporal Shift Module (TSM):
- Description: TSM introduces temporal shifts to efficiently capture temporal information. It has shown effectiveness in real-time action recognition.
- Implementation: Incorporate TSM into your model architecture for real-time processing of pose features.
When implementing these models, pay attention to model size, computational efficiency, and the ability to handle the real-time constraints of your application. Additionally, consider optimizations such as model quantization and hardware acceleration to further enhance real-time performance. Experimenting with different models and techniques will help you find the best balance between accuracy and speed for your specific use case.
Additional Considerations:
Dataset for Fighting Actions:
Curate or collect a comprehensive dataset containing diverse examples of fighting actions, including the specific actions you want to recognize. This dataset will be crucial for training and evaluating the recognition model. Pose Estimation Model Selection:
Choose a pose estimation model that is suitable for capturing fine-grained details relevant to fighting actions. Ensure that the model can run efficiently in real-time. Feature Extraction for Fighting Actions:
Develop a feature extraction process that highlights key pose landmarks relevant to distinguishing between different fighting actions. Consider joint angles, body part trajectories, or any other relevant features. Action Recognition Model Design:
Design or select an action recognition model capable of effectively classifying fighting actions based on the extracted features. Deep learning models, such as CNNs or RNNs, may be suitable for capturing both spatial and temporal aspects of actions. Fine-Tuning and Training:
Fine-tune the selected action recognition model using the curated dataset, paying special attention to the recognition of low kicks, back kicks, roundhouse kicks, dodges, and other target actions. Real-Time Performance Optimization:
Optimize the entire pipeline for real-time processing to ensure low latency. This may involve model quantization, hardware acceleration, or other optimization techniques. Evaluation Metrics for Fighting Actions:
Define specific evaluation metrics tailored to the recognition of fighting actions. Accuracy, precision, recall, and F1 score can be used to assess the model's performance on different actions. User Interface for Visualization (Optional):
Consider implementing a user interface that displays the real-time input frames, pose estimation results, and the recognized fighting actions. Visualization can aid in debugging and user interaction. Next Steps:
Begin by obtaining or creating a dataset for training and testing purposes. Choose an appropriate pose estimation model and integrate it into your pipeline. Design and implement the feature extraction and action recognition components. Train the model on your curated dataset and fine-tune it to achieve optimal performance. Evaluate the system's performance using the defined metrics. Optimize the system for real-time processing and deploy as needed. This enhanced project goal provides a more specific focus on recognizing fighting actions, guiding you towards a targeted and successful implementation.
best Action Recognition Models to consider based on my goal "Develop a real-time fighting action recognition system that utilizes Mediapipe for pose estimation to extract landmark features from input frames captured by a camera. The system should accurately classify and identify various fighting actions, including low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. The focus is on creating a robust and efficient solution capable of real-time action recognition within a fighting context, leveraging the pose estimation capabilities of the Mediapipe library."
4 / 4
ChatGPT Given your specific goal of developing a real-time fighting action recognition system using Mediapipe for pose estimation, it's important to choose an action recognition model that complements the features extracted from the landmark data. Here are some popular action recognition models that you can consider:
3D CNNs (Three-Dimensional Convolutional Neural Networks):
Description: 3D CNNs are effective for capturing temporal information in video sequences. They have been successful in action recognition tasks by considering the spatial and temporal dimensions of the data. Implementation: You can design a 3D CNN architecture or use pre-trained models such as I3D (Inflated 3D ConvNet) for action recognition. Two-Stream Networks:
Description: Two-stream networks consist of separate spatial and temporal streams. The spatial stream processes static frames, while the temporal stream captures motion information. This architecture is well-suited for tasks involving both pose and action recognition. Implementation: Develop or use pre-trained two-stream networks and adapt them to your specific task. Transformer Models:
Description: Transformer models have shown success in capturing long-range dependencies in sequences. They are designed to handle sequential data effectively and could be beneficial for recognizing complex fighting actions. Implementation: Explore transformer-based architectures adapted for action recognition tasks. ActionAI:
Description: ActionAI is a real-time action recognition library that supports various pre-trained models for human action recognition. It could be a suitable option for your real-time fighting action recognition task. Link: ActionAI GitHub Repository C3D (Convolutional 3D):
Description: C3D is specifically designed for 3D convolution on video data. It can be effective in capturing both spatial and temporal features in the context of action recognition. Implementation: You can use pre-trained C3D models or design a custom C3D architecture for your task. Real-Time Multi-Person Pose Estimation (RAP):
Description: RAP is a real-time pose-based action recognition model. It directly processes pose data for action recognition and may align well with your goal of leveraging the pose estimation capabilities of Mediapipe. Link: RAP GitHub Repository Custom Neural Network Architecture:
Description: Depending on the complexity of the fighting actions you want to recognize, consider designing a custom neural network architecture that combines pose features extracted from Mediapipe with an effective action recognition component.
Extracting the right human pose features is crucial for training an AI to recognize specific fighting poses. Here are some of the best features to consider:
Joint angles:
Angles of key joints like elbows, knees, wrists, and ankles: These angles often reveal specific configurations associated with different fighting moves. For example, a large extension angle of the elbow joint and a forward position of the wrist joint can indicate a jab punch. Angles between adjacent joints: Analyzing the relative angles between connected joints can further refine the pose and identify subtle differences between fighting techniques. Temporal changes in joint angles: Tracking the changes in joint angles over time provides valuable information about the dynamics of fighting movements like kicks, punches, and blocks. Relative positions:
Distances between keypoints: Calculating the distance between keypoints helps understand the spatial relationships between body parts and identify specific postures within fighting styles. Relative directions between keypoints: Analyzing the direction vectors between keypoints provides information about the orientation of body parts and their relative movement patterns. Body orientation:
Overall body orientation: Determining the direction the body is facing based on keypoints like shoulders and hips helps distinguish attacks directed towards specific targets. Orientation of specific body parts: Analyzing the orientation of individual limbs like arms and legs can reveal subtle details about the intended action or defensive posture. Movement trajectories:
Displacement of keypoints over time: Tracking the movement paths of keypoints helps identify dynamic fighting moves and analyze their spatial patterns. Velocity and acceleration of movement: Analyzing the speed and acceleration of keypoint movement provides additional insights into the dynamics and intensity of fighting actions. Additional features:
Hand and finger postures: Analyzing hand and finger positions can reveal specific techniques like gripping, grabbing, or blocking actions used in various fighting styles. Footwork patterns: Tracking the movement of feet and legs helps identify stances, footwork patterns, and specific fighting techniques that involve stepping and maneuvering. Pressure distribution: If available, pressure sensors or other technologies can provide information about the distribution of force and weight across the body, offering valuable insights into movement and impact during fighting. Important considerations:
Relevance to specific fighting styles: Choose features that are most relevant to the specific fighting styles you want the AI to recognize. Balance between complexity and accuracy: Aim for features that provide sufficient information for accurate pose recognition while avoiding unnecessary complexity that can increase computational cost. Data quality and annotation: Ensure your training data includes accurate and detailed annotations of the desired fighting poses for effective feature extraction and model training. By carefully selecting and extracting the most relevant and informative human pose features, you can train an AI to achieve accurate recognition of specific fighting poses and unlock various applications in training, analysis, and performance evaluation.
I'm thinking of using unsupervised learning methods for fast calibration
Clustering:
K-Means Clustering: If you have a large dataset of pose landmarks and want to group similar poses together, K-Means clustering can be useful. This can help identify common poses or movements in an unsupervised manner. Dimensionality Reduction:
Principal Component Analysis (PCA): PCA can be applied to reduce the dimensionality of the pose landmark features while retaining the most important information. This is particularly useful for visualization or when dealing with high-dimensional data. Time Series Analysis:
Dynamic Time Warping (DTW): If your pose landmarks represent a sequence of movements over time, DTW can be used to measure the similarity between different sequences. This is valuable for recognizing and comparing dynamic movements.
Graph-based Methods:
Graph Neural Networks (GNNs): If your pose landmarks can be represented as a graph structure (e.g., body joints connected by edges), GNNs can capture dependencies between joints and learn meaningful representations.
Anomaly Detection:
Isolation Forest or One-Class SVM: If you are interested in identifying anomalous or outlier poses, these methods can be applied to detect unusual patterns in the data.
or this project goal ""Develop a real-time fighting action recognition system that utilizes Mediapipe for pose estimation to extract landmark features from input frames captured by a camera. The system should accurately classify and identify various fighting actions, including low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. The focus is on creating a robust and efficient solution capable of real-time action recognition within a fighting context, leveraging the pose estimation capabilities of the Mediapipe library." Im using mediapipe and then slowfast networks ChatGPT Great choice! Combining Mediapipe for pose estimation with SlowFast networks for action recognition is a powerful approach for your real-time fighting action recognition system. Here's how you can proceed:
Integration Steps:
Mediapipe Pose Estimation:
Implement Mediapipe to perform real-time pose estimation on input frames from the camera. This will provide you with accurate landmark information for each frame. Feature Extraction from Pose Landmarks:
Extract relevant features from the pose landmarks obtained from Mediapipe. These features should capture important information about the positions and movements of body parts, which will be essential for recognizing different fighting actions. SlowFast Network for Action Recognition:
Choose or implement a SlowFast network architecture for action recognition. SlowFast networks are specifically designed to capture both spatial and temporal features efficiently. For implementation, you can use existing implementations, such as the SlowFast module in the Detectron2 library or custom implementations in deep learning frameworks like PyTorch. Dataset Preparation:
Curate or collect a dataset of labeled examples that include various fighting actions such as low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. This dataset will be used for training and evaluating your model. Training:
Train the SlowFast network on your dataset, using the feature vectors extracted from pose landmarks as input. Fine-tune the model to recognize the specific fighting actions of interest. Optimization for Real-Time Processing:
Optimize the entire pipeline, including pose estimation and action recognition components, to ensure real-time processing. Consider optimizations such as model quantization, hardware acceleration, or other techniques to achieve low latency. Evaluation Metrics:
Define evaluation metrics to assess the performance of your system. Metrics may include accuracy, precision, recall, and F1 score for each recognized action. User Interface (Optional):
Implement a user interface that displays the real-time input frames, the results of pose estimation, and the recognized fighting actions. Visualization can aid in monitoring and debugging. Testing and Iteration:
Test your real-time fighting action recognition system on various scenarios to ensure robustness. Iterate on the model and system based on feedback and performance evaluations. Deployment:
Once satisfied with the performance, deploy your real-time system for action recognition in a fighting context. By combining the strengths of Mediapipe for accurate pose estimation and SlowFast networks for efficient action recognition, you can create a robust and effective solution for real-time fighting action recognition. Make sure to document your process, experiment with different configurations, and continuously refine your system for optimal performance.
###For this project goal ""Develop a real-time fighting action recognition system that utilizes Mediapipe for pose estimation to extract landmark features from input frames captured by a camera. The system should accurately classify and identify various fighting actions, including low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. The focus is on creating a robust and efficient solution capable of real-time action recognition within a fighting context, leveraging the pose estimation capabilities of the Mediapipe library." comprehensive list of all features i should extract from mediapipe pose landmarks to train slowfast networks with Comprehensive list of features to extract from Mediapipe pose landmarks for training SlowFast networks for fighting action recognition: Joint coordinates:
3D coordinates of all 33 Mediapipe keypoints. Derivatives of joint coordinates (velocity and acceleration). Relative coordinates between specific joints (e.g., angle between elbow and wrist). Joint distances (e.g., distance between left and right shoulders). Motion features:
Joint angles and their derivatives over time (e.g., change in knee angle during a kick). Joint velocities and accelerations over time. Joint angular velocities and accelerations over time. Segmental angular velocities and accelerations (e.g., angular velocity of the upper arm during a punch). Spatial and temporal gradients of joint coordinates and angles. Pose statistics:
Center of mass (COM) trajectory and its derivatives. Body orientation and its derivatives (e.g., yaw, pitch, roll). Extremity motion statistics (e.g., average velocity of the hands during a block). Joint motion entropy and complexity measures. Action-specific features:
Specific joint trajectories for key fighting actions (e.g., trajectory of the foot during a low kick). Action-specific joint angles and their derivatives (e.g., knee angle during a roundhouse kick). Time-to-contact (TTC) estimation for specific body parts (e.g., time for a kick to reach the target). Action phase recognition (e.g., identifying preparation, execution, and recovery phases of a punch). Additional features:
Optical flow features for capturing overall motion patterns. Skeletonization features to represent the pose as a stick figure. Silhouette features to capture the overall body shape and motion. Action context features (e.g., opponent's actions, timing of actions). Note:
The specific features you choose will depend on the specific fighting actions you want to recognize and the performance requirements of your system. Experiment with different feature combinations and evaluate their impact on accuracy and efficiency. Consider using dimensionality reduction techniques to reduce the number of features and improve training efficiency. Here are some additional tips for extracting features from Mediapipe pose landmarks:
Use efficient data structures to store and process joint coordinates and features. Normalize joint coordinates and features to a common range. Apply smoothing filters to remove noise from joint trajectories. Segment the data into action sequences for training and evaluation. By carefully choosing and extracting relevant features from Mediapipe pose landmarks, you can train SlowFast networks for accurate and efficient real-time fighting action recognition. Remember to experiment and optimize your approach to achieve the best results for your specific project goals.
python code to extract 3D coordinates of all 33 Mediapipe keypoints. View other drafts
Python code to extract 3D coordinates of all 33 Mediapipe keypoints Python import mediapipe as mp
def extract_3d_keypoints(image, results): """ Extracts 3D coordinates of all 33 Mediapipe keypoints from an image and pose estimation results.
Args: image: Input image in RGB format. results: Mediapipe Pose detection results.
Returns: A list of 3D coordinates for all 33 keypoints, ordered by the Mediapipe keypoint index. """
landmarks = results.pose_landmarks.landmark
keypoints_3d = [] for landmark in landmarks: keypoints_3d.append([landmark.x, landmark.y, landmark.z])
return keypoints_3d
mp_drawing = mp.solutions.drawing_utils mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
image = cv2.imread("path/to/image.jpg")
results = pose.process(image)
keypoints_3d = extract_3d_keypoints(image, results)
for i, keypoint in enumerate(keypoints_3d): print(f"Keypoint {i}: {keypoint}")
mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS) cv2.imshow("Image with keypoints", image) cv2.waitKey(0) cv2.destroyAllWindows() Use code with caution. Learn more This code first imports the necessary libraries, including mediapipe. It then defines a function extract_3d_keypoints that takes an image and pose estimation results as input and returns a list of 3D coordinates for all 33 keypoints. The function iterates over each landmark in the results and extracts its 3D coordinates.
The example usage demonstrates how to read an image, perform pose estimation using Mediapipe, extract 3D keypoints, and optionally draw keypoints on the image.
Remember to install the necessary libraries and adjust the code according to your specific needs
python code to calculate Derivatives of joint coordinates (velocity and acceleration). View other drafts
Here is the Python code to calculate the derivatives of joint coordinates (velocity and acceleration) from a list of keypoints:
Python def calculate_derivatives(keypoints): """ Calculates the velocity and acceleration of each joint in a list of keypoints.
Args: keypoints: A list of 3D coordinates for all 33 keypoints, ordered by the Mediapipe keypoint index.
Returns: A tuple containing two lists: - velocities: A list of 3D velocities for all keypoints. - accelerations: A list of 3D accelerations for all keypoints. """
velocities = [] for i in range(1, len(keypoints)): previous_keypoint = keypoints[i-1] current_keypoint = keypoints[i] velocity = np.subtract(current_keypoint, previous_keypoint) velocities.append(velocity)
accelerations = [] if len(keypoints) >= 3: for i in range(2, len(keypoints)): two_frames_back = keypoints[i-2] previous_keypoint = keypoints[i-1] current_keypoint = keypoints[i] acceleration = np.subtract(2 * previous_keypoint - two_frames_back, current_keypoint) accelerations.append(acceleration) else: # Not enough frames for acceleration calculation accelerations = [np.zeros(3) for _ in range(len(keypoints) - 2)]
return velocities, accelerations
keypoints_3d = [...] # List of 3D keypoints
velocities, accelerations = calculate_derivatives(keypoints_3d)
for i, (velocity, acceleration) in enumerate(zip(velocities, accelerations)): print(f"Keypoint {i}: velocity: {velocity}, acceleration: {acceleration}") Use code with caution. Learn more This code first defines a function calculate_derivatives that takes a list of keypoints as input and returns two lists: one for velocities and one for accelerations. It iterates over the keypoints and calculates the difference between successive frames to obtain velocities. For accelerations, it requires two previous frames and uses the formula a = 2v[t-1] - v[t-2] to calculate the acceleration for each keypoint.
The example usage demonstrates how to calculate velocities and accelerations from a list of keypoints. Remember to replace [...] with your actual list of keypoints and adjust the code according to your specific needs.
python code to calculate the Relative coordinates between joints View other drafts
Here is the Python code to calculate the relative coordinates between joints using Mediapipe:
Python import mediapipe as mp
def calculate_relative_coordinates(landmark_a, landmark_b): """ Calculates the relative coordinates between two Mediapipe landmarks.
Args: landmark_a: A list containing the 3D coordinates of landmark A (x, y, z). landmark_b: A list containing the 3D coordinates of landmark B (x, y, z).
Returns: A list containing the relative coordinates of landmark A with respect to landmark B (dx, dy, dz). """ dx = landmark_a[0] - landmark_b[0] dy = landmark_a[1] - landmark_b[1] dz = landmark_a[2] - landmark_b[2] return [dx, dy, dz]
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
image = cv2.imread("path/to/image.jpg")
results = pose.process(image)
landmarks = results.pose_landmarks.landmark
elbow_idx = mp_pose.PoseLandmark.LEFT_ELBOW wrist_idx = mp_pose.PoseLandmark.LEFT_WRIST
elbow_landmark = landmarks[elbow_idx] wrist_landmark = landmarks[wrist_idx]
relative_coords = calculate_relative_coordinates(elbow_landmark, wrist_landmark)
print(f"Relative coordinates of elbow with respect to wrist: {relative_coords}") Use code with caution. Learn more This code defines a function calculate_relative_coordinates that takes two Mediapipe landmarks as input and calculates the relative coordinates of the first landmark with respect to the second. It subtracts the corresponding coordinates of each landmark to obtain the relative difference.
The example usage demonstrates how to:
Initialize Mediapipe Pose and read an image. Perform pose estimation and extract landmarks. Specify the desired pair of joints (elbow and wrist in this example). Use the calculate_relative_coordinates function to get the relative coordinates between the specified joints. Remember to adjust the code according to your specific needs, such as choosing different pairs of joints or performing calculations for all pairs.
python code to calculate Joint distances (e.g., distance between left and right shoulders) View other drafts
Here is the Python code to calculate the joint distances between two Mediapipe landmarks:
Python import mediapipe as mp import math
def calculate_joint_distance(landmark_a, landmark_b): """ Calculates the Euclidean distance between two Mediapipe landmarks.
Args: landmark_a: A list containing the 3D coordinates of landmark A (x, y, z). landmark_b: A list containing the 3D coordinates of landmark B (x, y, z).
Returns: The Euclidean distance between landmark A and landmark B. """ dx = landmark_a[0] - landmark_b[0] dy = landmark_a[1] - landmark_b[1] dz = landmark_a[2] - landmark_b[2] return math.sqrt(dx2 + dy2 + dz**2)
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
image = cv2.imread("path/to/image.jpg")
results = pose.process(image)
landmarks = results.pose_landmarks.landmark
left_shoulder_idx = mp_pose.PoseLandmark.LEFT_SHOULDER right_shoulder_idx = mp_pose.PoseLandmark.RIGHT_SHOULDER
left_shoulder_landmark = landmarks[left_shoulder_idx] right_shoulder_landmark = landmarks[right_shoulder_idx]
distance = calculate_joint_distance(left_shoulder_landmark, right_shoulder_landmark)
print(f"Distance between left and right shoulders: {distance}")
mp_drawing = mp.solutions.drawing_utils mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS) cv2.putText(image, f"Distance: {distance:.2f}", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) cv2.imshow("Image with landmarks and distance", image) cv2.waitKey(0) cv2.destroyAllWindows() Use code with caution. Learn more This code defines a function calculate_joint_distance which takes two Mediapipe landmarks as input and calculates the Euclidean distance between them:
It subtracts the corresponding coordinates of each landmark to obtain the differences in x, y, and z directions. It uses the Pythagorean theorem to calculate the square root of the sum of squares of these differences, which represents the Euclidean distance. The example demonstrates how to:
Initialize Mediapipe Pose and read an image. Perform pose estimation and extract landmarks. Specify the desired pair of joints (left and right shoulders in this example). Use the calculate_joint_distance function to get the distance between the specified joints. Display the distance on the image and draw landmarks for visualization. Remember to adjust the code according to your specific needs, such as choosing different pairs of joints or calculating distances for all joint combinations.
Here is the Python code to calculate one of the motion features, joint angles and their derivatives over time:
Python import mediapipe as mp import numpy as np
def calculate_joint_angles(landmarks, pose_connections): """ Calculates the angles between connected joints based on their 3D coordinates.
Args: landmarks: A list of 3D coordinates for all 33 Mediapipe keypoints. pose_connections: A list of pairs representing the connections between keypoints.
Returns: A dictionary containing the angles for each connected joint pair. """ joint_angles = {} for connection in pose_connections: joint_a_idx, joint_b_idx = connection joint_a = landmarks[joint_a_idx] joint_b = landmarks[joint_b_idx]
# Calculate vector components
dx = joint_b[0] - joint_a[0]
dy = joint_b[1] - joint_a[1]
dz = joint_b[2] - joint_a[2]
# Calculate angle based on vector components
angle = math.atan2(dy, dx) * 180 / math.pi
# Store the angle in the dictionary
joint_angles[connection] = angle
return joint_angles
def calculate_joint_angle_derivatives(joint_angles, window_size): """ Calculates the derivatives of joint angles over time using a sliding window.
Args: joint_angles: A dictionary containing the angles for each connected joint pair. window_size: The size of the sliding window used for derivative calculation.
Returns: A dictionary containing the derivatives of angles for each connected joint pair. """ joint_angle_derivatives = {} for connection, angle_sequence in joint_angles.items(): # Apply sliding window to calculate derivatives derivatives = np.diff(angle_sequence, prepend=angle_sequence[0] * (window_size - 1))
# Store derivatives in the dictionary
joint_angle_derivatives[connection] = derivatives[:len(angle_sequence) - window_size + 1]
return joint_angle_derivatives
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
pose_connections = mp_pose.POSE_CONNECTIONS
images = [cv2.imread(f"path/to/image_{i}.jpg") for i in range(10)]
landmarks_list = [] for image in images:
results = pose.process(image)
landmarks = results.pose_landmarks.landmark landmarks_list.append(landmarks)
joint_angles_list = [] for landmarks in landmarks_list: joint_angles = calculate_joint_angles(landmarks, pose_connections) joint_angles_list.append(joint_angles)
window_size = 3 joint_angle_derivatives_list = [] for joint_angles in joint_angles_list: joint_angle_derivatives = calculate_joint_angle_derivatives(joint_angles, window_size) joint_angle_derivatives_list.append(joint_angle_derivatives)
Use code with caution. Learn more This code defines two functions:
calculate_joint_angles: This function takes the 3D coordinates of all landmarks and pose connections as input and calculates the angles between connected joint pairs using their vector components. It returns a dictionary containing the angles for each connection. calculate_joint_angle_derivatives: This function takes the dictionary of joint angles and a window size as input and calculates the derivatives of angles over time using a sliding window approach. It returns a new dictionary containing the derivatives of angles for each connection. The example demonstrates how to:
Initialize Mediapipe Pose and define pose connections. Read a sequence of images and extract 3D keypoints for each image. Calculate the joint angles for each frame. Calculate the joint angle derivatives for each connected joint pair using a sliding window of size 3. This code provides a basic framework for calculating joint angles
python code to calculate each of the following Motion features: Joint velocities and accelerations over time. View other drafts
Python code to calculate joint velocities and accelerations: Python import mediapipe as mp import math
def calculate_joint_velocity(previous_landmark, current_landmark, time_delta): """ Calculates the velocity of a joint based on its position in two consecutive frames.
Args: previous_landmark: A list containing the 3D coordinates of the joint at the previous frame (x, y, z). current_landmark: A list containing the 3D coordinates of the joint at the current frame (x, y, z). time_delta: The time difference between the two frames (in seconds).
Returns: A list containing the velocity of the joint in 3D space (vx, vy, vz). """ vx = (current_landmark[0] - previous_landmark[0]) / time_delta vy = (current_landmark[1] - previous_landmark[1]) / time_delta vz = (current_landmark[2] - previous_landmark[2]) / time_delta return [vx, vy, vz]
def calculate_joint_acceleration(previous_velocity, current_velocity, time_delta): """ Calculates the acceleration of a joint based on its velocity in two consecutive frames.
Args: previous_velocity: A list containing the velocity of the joint at the previous frame (vx, vy, vz). current_velocity: A list containing the velocity of the joint at the current frame (vx, vy, vz). time_delta: The time difference between the two frames (in seconds).
Returns: A list containing the acceleration of the joint in 3D space (ax, ay, az). """ ax = (current_velocity[0] - previous_velocity[0]) / time_delta ay = (current_velocity[1] - previous_velocity[1]) / time_delta az = (current_velocity[2] - previous_velocity[2]) / time_delta return [ax, ay, az]
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
time_delta = 1.0 / 30.0
previous_landmarks = [] velocities = [] accelerations = []
for frame in capture.read_frames():
results = pose.process(frame)
landmarks = results.pose_landmarks.landmark
if len(previous_landmarks) > 0: for i, landmark in enumerate(landmarks): velocity = calculate_joint_velocity(previous_landmarks[i], landmark, time_delta) velocities.append(velocity) acceleration = calculate_joint_acceleration(velocities[i-1], velocity, time_delta) accelerations.append(acceleration)
previous_landmarks = landmarks
capture.release() cv2.destroyAllWindows() Use code with caution. Learn more This code defines two functions:
calculate_joint_velocity: This function calculates the velocity of a joint based on its position in two consecutive frames and the time difference between them. calculate_joint_acceleration: This function calculates the acceleration of a joint based on its velocity in two consecutive frames and the time difference between them. The example demonstrates how to:
Initialize Mediapipe Pose and capture video frames. Set the time delta based on the desired frame rate. Initialize data structures to store previous landmarks, velocities, and accelerations. Loop through each frame: Perform pose estimation. Extract landmarks. Calculate velocities and accelerations for each landmark using the defined functions. Store current landmarks for the next frame. Process and analyze the calculated velocities and accelerations. (Optional) Visualize the velocities and accelerations. Release resources. Remember to adjust the code according to your specific needs, such as choosing different joints, visualizing the results, and integrating them into your SlowFast training process.
python code to calculate each of the following Motion features:
Segmental angular velocities and accelerations (e.g., angular velocity of the upper arm during a punch).
View other drafts
Here is the Python code to calculate segmental angular velocities and accelerations for a specific body segment during a punch:
Python import mediapipe as mp from scipy.spatial.transform import Rotation as R import numpy as np
def calculate_segmental_angular_velocities(landmarks_prev, landmarks_curr, segment_indices): """ Calculates the segmental angular velocities for a specific body segment using two consecutive sets of Mediapipe landmarks.
Args: landmarks_prev: A list of 3D coordinates for all 33 Mediapipe landmarks at the previous frame. landmarks_curr: A list of 3D coordinates for all 33 Mediapipe landmarks at the current frame. segment_indices: A list containing the indices of the two landmarks defining the body segment (e.g., [mp_pose.PoseLandmark.LEFT_SHOULDER, mp_pose.PoseLandmark.LEFT_ELBOW]).
Returns: A list containing the segmental angular velocities for each axis (x, y, z). """
landmark_a_prev = landmarks_prev[segment_indices[0]] landmark_b_prev = landmarks_prev[segment_indices[1]] landmark_a_curr = landmarks_curr[segment_indices[0]] landmark_b_curr = landmarks_curr[segment_indices[1]]
vector_prev = landmark_b_prev - landmark_a_prev vector_curr = landmark_b_curr - landmark_a_curr
q_prev = R.from_matrix(np.array([vector_prev, [0, 0, 0], [0, 0, 0]]).T) q_curr = R.from_matrix(np.array([vector_curr, [0, 0, 0], [0, 0, 0]]).T)
dq = q_curr * q_prev.inv()
dt = 1 # Assuming a constant time interval between frames angular_velocities = dq.as_rotvec() * 2 / dt return angular_velocities
mp_pose = mp.solutions.pose
segment_indices = [mp_pose.PoseLandmark.LEFT_SHOULDER, mp_pose.PoseLandmark.LEFT_ELBOW]
Use code with caution. Learn more This code defines a function calculate_segmental_angular_velocities which takes three arguments:
landmarks_prev: A list containing the 3D coordinates of all 33 Mediapipe landmarks at the previous frame. landmarks_curr: A list containing the 3D coordinates of all 33 Mediapipe landmarks at the current frame. segment_indices: A list containing the indices of the two landmarks defining the body segment (e.g., left shoulder and left elbow for an upper arm segment). The function performs the following steps:
Extracts the 3D coordinates of the relevant landmarks for the segment. Calculates the vectors representing the segment orientation for both the previous and current frames. Converts the vectors to quaternions for rotation representation. Calculates the relative rotation quaternion from the current to the previous frame. Extracts the angular velocity from the relative rotation quaternion and scales it according to the assumed time interval. The example usage demonstrates how to define the specific segment indices and calculate segmental angular velocities for each frame. You can further process the data by differentiating the angular velocities to obtain segmental angular accelerations. Remember to adapt the code to your specific needs, including adjusting the time interval and handling potential noise in the data.
python code to calculate each of the following Motion features:
Spatial and temporal gradients of joint coordinates and angles
View other drafts
Here is the Python code to calculate the spatial and temporal gradients of joint coordinates and angles:
Python import numpy as np
def calculate_gradients(joint_coords, joint_angles): """ Calculates the spatial and temporal gradients of joint coordinates and angles.
Args: joint_coords: A NumPy array of shape (frames, keypoints, 3) containing 3D joint coordinates for all frames and keypoints. joint_angles: A NumPy array of shape (frames, keypoints) containing joint angles for all frames and keypoints.
Returns: A dictionary containing the following features: - spatial_coords_grad: Spatial gradients of joint coordinates. - temporal_coords_grad: Temporal gradients of joint coordinates. - spatial_angle_grad: Spatial gradients of joint angles. - temporal_angle_grad: Temporal gradients of joint angles. """ features = {}
spatial_coords_grad = np.diff(joint_coords, axis=1) features["spatial_coords_grad"] = spatial_coords_grad
temporal_coords_grad = np.diff(joint_coords, axis=0) features["temporal_coords_grad"] = temporal_coords_grad
spatial_angle_grad = np.diff(joint_angles, axis=1) features["spatial_angle_grad"] = spatial_angle_grad
temporal_angle_grad = np.diff(joint_angles, axis=0) features["temporal_angle_grad"] = temporal_angle_grad
return features
joint_coords = ... # (frames, keypoints, 3) array of 3D joint coordinates joint_angles = ... # (frames, keypoints) array of joint angles
features = calculate_gradients(joint_coords, joint_angles)
spatial_coords_grad = features["spatial_coords_grad"] temporal_coords_grad = features["temporal_coords_grad"] spatial_angle_grad = features["spatial_angle_grad"] temporal_angle_grad = features["temporal_angle_grad"]
Use code with caution. Learn more This code defines a function calculate_gradients that takes two NumPy arrays as input:
joint_coords: A 3D array containing joint coordinates for all frames and keypoints. joint_angles: A 2D array containing joint angles for all frames and keypoints. The function then calculates and stores the following features in a dictionary:
spatial_coords_grad: Spatial gradients of joint coordinates, calculated using np.diff along the keypoint dimension. temporal_coords_grad: Temporal gradients of joint coordinates, calculated using np.diff along the frame dimension. spatial_angle_grad: Spatial gradients of joint angles, calculated using np.diff along the keypoint dimension. temporal_angle_grad: Temporal gradients of joint angles, calculated using np.diff along the frame dimension. The example usage demonstrates how to use this function with your extracted joint coordinates and angles. You can then use these calculated gradients as additional features for further analysis and training your fighting action recognition model.
python code to calculate in mediapipe context Pose statistics: Center of mass (COM) trajectory and its derivatives.
import mediapipe as mp
import numpy as np
def calculate_com_trajectory(landmarks):
"""
Calculates the center of mass (COM) trajectory and its derivatives from Mediapipe landmarks.
Args:
landmarks: A list of Mediapipe landmarks for each frame in a sequence. Each landmark is a list containing 3D coordinates (x, y, z).
Returns:
- com_trajectory: A NumPy array of shape (frames, 3) containing the COM coordinates for each frame.
- com_velocity: A NumPy array of shape (frames, 3) containing the velocity of the COM for each frame.
- com_acceleration: A NumPy array of shape (frames, 3) containing the acceleration of the COM for each frame.
"""
# Calculate COM for each frame
com_trajectory = []
for frame in landmarks:
com_x = np.mean([landmark[0] for landmark in frame])
com_y = np.mean([landmark[1] for landmark in frame])
com_z = np.mean([landmark[2] for landmark in frame])
com_trajectory.append([com_x, com_y, com_z])
com_trajectory = np.array(com_trajectory)
# Calculate COM velocity
com_velocity = np.diff(com_trajectory, axis=0)
# Calculate COM acceleration
com_acceleration = np.diff(com_velocity, axis=0)
return (com_trajectory, com_velocity, com_acceleration)
# Example usage
mp_pose = mp.solutions.pose
# Initialize Mediapipe Pose
pose = mp_pose.Pose()
# Read input video
cap = cv2.VideoCapture("path/to/video.mp4")
# Extract landmarks for each frame
landmarks_list = []
while cap.isOpened():
ret, image = cap.read()
if ret:
results = pose.process(image)
if results.pose_landmarks:
landmarks = results.pose_landmarks.landmark
landmarks_list.append(landmarks)
else:
break
# Calculate COM trajectory and derivatives
com_trajectory, com_velocity, com_acceleration = calculate_com_trajectory(landmarks_list)
# Use these features for further analysis and trainingThis code defines a function calculate_com_trajectory that takes a list of Mediapipe landmarks as input and returns:
- com_trajectory: A NumPy array containing the COM coordinates for each frame.
- com_velocity: A NumPy array containing the velocity of the COM for each frame.
- com_acceleration: A NumPy array containing the acceleration of the COM for each frame.
The example usage demonstrates how to:
- Initialize Mediapipe Pose and read an input video.
- Extract landmarks for each frame in the video.
- Call the
calculate_com_trajectoryfunction to obtain the COM trajectory and derivatives. - Use these features for further analysis and training your fighting action recognition model.
This code provides a basic framework for calculating COM-related features. You can further enhance it by:
- Filtering the COM trajectory to remove noise.
- Calculating additional statistics like COM displacement, average speed, and movement direction.
- Combining COM features with other pose features for improved recognition performance.
python code to calculate in mediapipe context Pose statistics: Body orientation and its derivatives (e.g., yaw, pitch, roll). View other drafts
Python code to calculate body orientation and its derivatives in Mediapipe Python import mediapipe as mp import numpy as np
def calculate_body_orientation(landmarks): """ Calculates body orientation (yaw, pitch, roll) from Mediapipe landmarks.
Args: landmarks: A list containing 3D coordinates of all 33 Mediapipe landmarks.
Returns: A list containing yaw, pitch, and roll angles in degrees. """
head_top_idx = mp_pose.PoseLandmark.HEAD_TOP neck_idx = mp_pose.PoseLandmark.NECK shoulder_left_idx = mp_pose.PoseLandmark.LEFT_SHOULDER shoulder_right_idx = mp_pose.PoseLandmark.RIGHT_SHOULDER
head_top = landmarks[head_top_idx] neck = landmarks[neck_idx] shoulder_left = landmarks[shoulder_left_idx] shoulder_right = landmarks[shoulder_right_idx]
neck_to_head = head_top - neck neck_to_shoulder_left = shoulder_left - neck neck_to_shoulder_right = shoulder_right - neck
rotation_matrix_y, rotation_matrix_x, rotation_matrix_z = mp_pose.utils.calculate_rotation_matrix(neck_to_shoulder_left, neck_to_head, neck_to_shoulder_right)
yaw, pitch, roll = mp_pose.utils.extract_yaw_pitch_roll_from_rotation_matrix(rotation_matrix_y, rotation_matrix_x, rotation_matrix_z)
yaw_deg = np.rad2deg(yaw) pitch_deg = np.rad2deg(pitch) roll_deg = np.rad2deg(roll)
return [yaw_deg, pitch_deg, roll_deg]
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
image = cv2.imread("path/to/image.jpg")
results = pose.process(image)
landmarks = results.pose_landmarks.landmark
orientation = calculate_body_orientation(landmarks)
print(f"Body orientation: yaw = {orientation[0]}, pitch = {orientation[1]}, roll = {orientation[2]}")
mp_drawing = mp.solutions.drawing_utils mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS) mp_drawing.draw_axis(image, results.pose_landmarks.landmark[mp_pose.PoseLandmark.NECK], results.pose_landmarks.landmark[mp_pose.PoseLandmark.HEAD_TOP], results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER]) cv2.imshow("Image with landmarks and orientation", image) cv2.waitKey(0) cv2.destroyAllWindows() Use code with caution. Learn more This code defines a function calculate_body_orientation that takes a list of 3D landmark coordinates as input and calculates the body orientation in terms of yaw, pitch, and roll angles:
It defines key landmarks used for orientation estimation: head top, neck, left shoulder, and right shoulder. It extracts the 3D coordinates of these landmarks. It calculates vectors representing neck to head, neck to left shoulder, and neck to right shoulder. It utilizes Mediapipe's internal functions to calculate three rotation matrices based on the defined vectors. It extracts yaw, pitch, and roll angles from the rotation matrices using another Mediapipe function. Finally, it converts the angles from radians to degrees and returns them as a list. The example demonstrates how to calculate and display the body orientation from a given image. You can use this code to calculate body orientation for each frame in your fighting action sequences and incorporate it as a feature for training your model.
python code to calculate in mediapipe context Pose statistics: Extremity motion statistics (e.g., average velocity of the hands during a block). Here's the Python code to calculate extremity motion statistics in Mediapipe, specifically average velocity of hands during a block:
Python import mediapipe as mp import math
def calculate_extremity_motion_stats(landmarks, block_start_frame, block_end_frame): """ Calculates the average velocity of hands during a blocking action.
Args: landmarks: A list of Mediapipe pose landmarks for all frames. block_start_frame: Frame index where the blocking action starts. block_end_frame: Frame index where the blocking action ends.
Returns: A dictionary containing the average hand velocities for both hands. """
left_hand_landmarks = [landmarks[frame][mp_pose.PoseLandmark.LEFT_WRIST] for frame in range(block_start_frame, block_end_frame + 1)] right_hand_landmarks = [landmarks[frame][mp_pose.PoseLandmark.RIGHT_WRIST] for frame in range(block_start_frame, block_end_frame + 1)]
left_hand_velocities = [] for i in range(1, len(left_hand_landmarks)): dx = left_hand_landmarks[i][0] - left_hand_landmarks[i-1][0] dy = left_hand_landmarks[i][1] - left_hand_landmarks[i-1][1] dz = left_hand_landmarks[i][2] - left_hand_landmarks[i-1][2] velocity = math.sqrt(dx2 + dy2 + dz**2) left_hand_velocities.append(velocity)
right_hand_velocities = [] for i in range(1, len(right_hand_landmarks)): dx = right_hand_landmarks[i][0] - right_hand_landmarks[i-1][0] dy = right_hand_landmarks[i][1] - right_hand_landmarks[i-1][1] dz = right_hand_landmarks[i][2] - right_hand_landmarks[i-1][2] velocity = math.sqrt(dx2 + dy2 + dz**2) right_hand_velocities.append(velocity)
avg_left_hand_vel = sum(left_hand_velocities) / len(left_hand_velocities) avg_right_hand_vel = sum(right_hand_velocities) / len(right_hand_velocities)
return {"left_hand_vel": avg_left_hand_vel, "right_hand_vel": avg_right_hand_vel}
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
video = cv2.VideoCapture("path/to/video.mp4")
block_start_frame = 100 # Block starts at frame 100 block_end_frame = 150 # Block ends at frame 150 extremity_stats_list = []
while True: ret, frame = video.read() if not ret: break
results = pose.process(frame)
landmarks = results.pose_landmarks.landmark
if block_start_frame <= video.get(cv2.CAP_PROP_POS_FRAMES) <= block_end_frame: extremity_stats = calculate_extremity_motion_stats(landmarks, block_start_frame, block_end_frame) extremity_stats_list.append(extremity_stats)
video.release() cv2.destroyAllWindows() Use code with caution. Learn more This code defines a function calculate_extremity_motion_stats that takes:
A list of Mediapipe pose landmarks for all frames. The start and end frame indices of the block action. It calculates the average velocity of both hands during the block by:
Extracting hand landmarks for the specified frames. Calculating the Euclidean distance between consecutive hand landmarks to obtain velocities. Averaging all velocities for each hand. The example usage demonstrates how to process video frames, extract landmarks, calculate extremity statistics during the specified
profile picture other reasons i woould want to calculate this View other drafts
There are several other reasons why you might want to calculate extremity motion statistics beyond the specific example of calculating average hand velocity during a block:
Analyzing movement patterns:
Understanding the overall motion of extremities can reveal insights into fighting styles, techniques, and individual differences in movement. By comparing the motion statistics of different actions, you can identify key differences and develop more robust action recognition models. Analyzing changes in motion statistics over time can be used to track fatigue, injury, or recovery progress. Improving action recognition accuracy:
Extremity motion statistics can be used as additional features for training and evaluating action recognition models. These features can capture subtle variations in movement that may not be easily captured by joint coordinates or angles alone. Combining motion statistics with other features can lead to more accurate and robust action recognition systems. Providing feedback and guidance:
By analyzing extremity motion statistics in real-time, you can provide feedback and guidance to users, such as athletes or martial artists. This feedback could help them improve their technique, avoid injury, and optimize their performance. This can be especially helpful in training situations where personalized feedback is crucial. Developing new training methodologies:
Understanding the specific movement patterns involved in different fighting actions can help develop more effective and targeted training programs. By analyzing motion statistics, you can identify areas where individuals need additional practice or focus. This can lead to more efficient and personalized training experiences. Evaluating the effectiveness of training programs:
By monitoring changes in extremity motion statistics over time, you can evaluate the effectiveness of different training programs. This can help identify which programs are most effective for improving specific skills and techniques. This data can be used to continuously improve and refine training programs for optimal results. Research and development:
Analyzing extremity motion statistics can be a valuable tool for researchers studying human movement and fighting techniques. This data can be used to develop new models of human movement, improve computer vision algorithms, and gain deeper insights into the biomechanics of fighting. Overall, calculating extremity motion statistics can be a valuable tool for various applications beyond just action recognition. By understanding the movement patterns of limbs and joints, you can gain valuable insights into performance, technique, and overall fighting style.
python code to calculate in mediapipe context Pose statistics: Joint motion entropy and complexity measures. View other drafts
Here is the Python code to calculate joint motion entropy and complexity measures in Mediapipe:
Python import mediapipe as mp import numpy as np from scipy.stats import entropy from pyts.approximation import SymbolicAggregateApproximation
def calculate_joint_motion_stats(landmarks, joint_idx): """ Calculates joint motion entropy and complexity measures.
Args: landmarks: A list of Mediapipe pose landmarks for all frames. joint_idx: The index of the specific joint of interest.
Returns: A dictionary containing the joint motion entropy and complexity measure. """
joint_coords = np.array([landmark[joint_idx] for landmark in landmarks])
x = np.diff(joint_coords[:, 0]) y = np.diff(joint_coords[:, 1]) z = np.diff(joint_coords[:, 2]) motion_entropy = np.mean([entropy(x), entropy(y), entropy(z)])
sax = SymbolicAggregateApproximation(n_bins=3, alphabet="baa") encoded_x = sax.fit_transform(x) sax_entropy = entropy(encoded_x)
complexity = 1 - sax_entropy
return {"motion_entropy": motion_entropy, "complexity": complexity}
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
video = cv2.VideoCapture("path/to/video.mp4")
joint_stats_list = [] while True: ret, frame = video.read() if not ret: break
results = pose.process(frame)
landmarks = results.pose_landmarks.landmark
joint_stats = calculate_joint_motion_stats(landmarks, mp_pose.PoseLandmark.LEFT_KNEE) joint_stats_list.append(joint_stats)
video.release() cv2.destroyAllWindows() Use code with caution. Learn more This code defines a function calculate_joint_motion_stats that takes:
A list of Mediapipe pose landmarks for all frames. The index of the specific joint of interest. It calculates the following:
Joint motion entropy: This measures the randomness and unpredictability of the joint movement. It is calculated using the Shannon entropy of the differences in joint coordinates over time. Joint motion complexity: This measures the intricacy and detail of the joint movement. It is calculated using Symbolic Aggregate Approximation (SAX) to convert the joint trajectory into a symbolic sequence and then calculating the entropy of the sequence. The example usage demonstrates how to process video frames, extract landmarks, and calculate joint motion statistics for a specific joint (e.g., left knee). You can analyze the calculated entropy and complexity values to gain insights into the movement patterns of that joint.
You can modify the code to calculate these statistics for multiple joints simultaneously or use different complexity measures like Approximate Entropy or Lempel-Ziv complexity. Remember to interpret the results in the context of your specific application and research goals.
Here's the Python code to calculate specific joint trajectories for key fighting actions in Mediapipe, using the example of a low kick:
Python import mediapipe as mp import math
def calculate_low_kick_trajectory(landmarks, low_kick_start_frame, low_kick_end_frame): """ Calculates the trajectory of the foot during a low kick.
Args: landmarks: A list of Mediapipe pose landmarks for all frames. low_kick_start_frame: Frame index where the low kick starts. low_kick_end_frame: Frame index where the low kick ends.
Returns: A dictionary containing the list of x, y, and z coordinates of the foot throughout the low kick. """
foot_landmark = mp_pose.PoseLandmark.RIGHT_ANKLE # Change for LEFT_ANKLE if needed
foot_trajectory = [] for frame in range(low_kick_start_frame, low_kick_end_frame + 1): foot_x = landmarks[frame][foot_landmark].x foot_y = landmarks[frame][foot_landmark].y foot_z = landmarks[frame][foot_landmark].z foot_trajectory.append([foot_x, foot_y, foot_z])
return {"foot_trajectory": foot_trajectory}
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
video = cv2.VideoCapture("path/to/video.mp4")
low_kick_start_frame = 100 # Low kick starts at frame 100 low_kick_end_frame = 150 # Low kick ends at frame 150 action_specific_features_list = []
while True: ret, frame = video.read() if not ret: break
results = pose.process(frame)
landmarks = results.pose_landmarks.landmark
if low_kick_start_frame <= video.get(cv2.CAP_PROP_POS_FRAMES) <= low_kick_end_frame: low_kick_features = calculate_low_kick_trajectory(landmarks, low_kick_start_frame, low_kick_end_frame) action_specific_features_list.append(low_kick_features)
video.release() cv2.destroyAllWindows() Use code with caution. Learn more This code defines a function calculate_low_kick_trajectory that takes:
A list of Mediapipe pose landmarks for all frames. The start and end frame indices of the low kick action. It calculates the trajectory of the foot by:
Extracting the relevant foot landmark coordinates for each frame during the low kick. Storing these coordinates in a list representing the foot's movement path over time. The example demonstrates how to:
Process video frames. Detect the low kick action based on specific frame ranges. Calculate the foot trajectory for each detected low kick. Store these action-specific features for further analysis. This code can be easily adapted to calculate trajectories for other key fighting actions by changing the targeted landmark and adjusting the action detection logic based on specific movement patterns.
python code to calculate in mediapipe context Action-Specific Features:
Action-specific joint angles and their derivatives (e.g., knee angle during a roundhouse kick).
View other drafts
Here's the Python code to calculate action-specific joint angles and their derivatives for a roundhouse kick:
Python import mediapipe as mp import math
def calculate_roundhouse_kick_features(landmarks, roundhouse_start_frame, roundhouse_end_frame): """ Calculates the knee angle and its derivatives during a roundhouse kick.
Args: landmarks: A list of Mediapipe pose landmarks for all frames. roundhouse_start_frame: Frame index where the roundhouse kick starts. roundhouse_end_frame: Frame index where the roundhouse kick ends.
Returns: A dictionary containing the knee angle and its derivatives for each frame of the roundhouse kick. """
hip_idx = mp_pose.PoseLandmark.RIGHT_HIP knee_idx = mp_pose.PoseLandmark.RIGHT_KNEE ankle_idx = mp_pose.PoseLandmark.RIGHT_ANKLE
knee_angles = [] for frame in range(roundhouse_start_frame, roundhouse_end_frame + 1): hip_landmark = landmarks[frame][hip_idx] knee_landmark = landmarks[frame][knee_idx] ankle_landmark = landmarks[frame][ankle_idx]
# Calculate vectors
hip_to_knee = np.array([knee_landmark.x - hip_landmark.x, knee_landmark.y - hip_landmark.y])
knee_to_ankle = np.array([ankle_landmark.x - knee_landmark.x, ankle_landmark.y - knee_landmark.y])
# Calculate angle using dot product and arccos
dot_product = np.dot(hip_to_knee, knee_to_ankle)
norm_hip_to_knee = np.linalg.norm(hip_to_knee)
norm_knee_to_ankle = np.linalg.norm(knee_to_ankle)
knee_angle = math.acos(dot_product / (norm_hip_to_knee * norm_knee_to_ankle))
knee_angles.append(knee_angle)
knee_angle_velocities = [] knee_angle_accelerations = [] for i in range(1, len(knee_angles)): velocity = knee_angles[i] - knee_angles[i-1] acceleration = velocity - knee_angle_velocities[i-1] if i > 1 else 0 knee_angle_velocities.append(velocity) knee_angle_accelerations.append(acceleration)
return { "knee_angles": knee_angles, "knee_angle_velocities": knee_angle_velocities, "knee_angle_accelerations": knee_angle_accelerations }
mp_pose = mp.solutions.pose
pose = mp_pose.Pose()
video = cv2.VideoCapture("path/to/video.mp4")
roundhouse_start_frame = 100 # Roundhouse kick starts at frame 100 roundhouse_end_frame = 150 # Roundhouse kick ends at frame 150 action_specific_features_list = []
while True: ret, frame = video.read() if not ret: break
results = pose.process(frame)
landmarks = results.pose_landmarks.landmark
if roundhouse_start_frame <= video.get(cv2.CAP_PROP_POS_FRAMES) <= roundhouse_end_frame: roundhouse_features = calculate_roundhouse_kick_features(landmarks, roundhouse_start_frame, roundhouse_end_frame) action_specific_features_list.append(roundhouse_features)
video.release() cv2.destroyAllWindows() Use code with caution. Learn more This code defines a function calculate_roundhouse_kick_features that takes:
A list of Mediapipe pose landmarks for all frames. The start and end frame indices of the roundhouse kick action. It calculates:
Knee angle: Using the dot product and arccos between vectors formed by hip
python code to calculate in mediapipe context Action-Specific Features:
Action phase recognition (e.g., preparation, execution, and recovery phases of a punch).
View other drafts
Python code to calculate Action-Specific Features in Mediapipe: Action phase recognition (preparation, execution, and recovery phases of a punch)
Python import mediapipe as mp import numpy as np
mp_drawing = mp.solutions.drawing_utils mp_pose = mp.solutions.pose
preparation_threshold = 0.8 # Elbow angle threshold for preparation phase execution_threshold = 0.1 # Elbow angle threshold for execution phase recovery_threshold = 0.6 # Elbow angle threshold for recovery phase
def get_action_phase(keypoints, frame_index): """ This function identifies the action phase of a punch based on the elbow angle.
Args: keypoints: A list of 3D keypoints extracted from Mediapipe. frame_index: The current frame index in the video sequence.
Returns: A string indicating the action phase ("preparation", "execution", or "recovery"). """
elbow_keypoint = keypoints[mp_pose.PoseLandmark.LEFT_ELBOW.value] shoulder_keypoint = keypoints[mp_pose.PoseLandmark.LEFT_SHOULDER.value] wrist_keypoint = keypoints[mp_pose.PoseLandmark.LEFT_WRIST.value]
elbow_vector = wrist_keypoint - elbow_keypoint shoulder_vector = elbow_keypoint - shoulder_keypoint elbow_angle = np.arccos(np.dot(elbow_vector, shoulder_vector) / (np.linalg.norm(elbow_vector) * np.linalg.norm(shoulder_vector)))
if frame_index < 10: phase = "preparation" elif elbow_angle < execution_threshold: phase = "execution" elif elbow_angle > recovery_threshold: phase = "recovery" else: phase = "transition"
return phase
def process_video(video_path):
pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) cap = cv2.VideoCapture(video_path)
while True: success, image = cap.read() if not success: break
# Extract keypoints
results = pose.process(image)
# Check for successful pose detection
if results.pose_landmarks:
# Calculate action phase
phase = get_action_phase(results.pose_landmarks, frame_index)
# Print action phase
print(f"Frame {frame_index}: Action phase - {phase}")
mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
# Display image
cv2.imshow('Mediapipe Pose', image)
if cv2.waitKey(5) & 0xFF == ord('q'):
break
cap.release() cv2.destroyAllWindows()
process_video("path/to/your/video.mp4") Use code with caution. Learn more This code demonstrates how to identify the preparation, execution, and recovery phases of a punch based on the elbow angle. The thresholds for each phase can be adjusted based on the specific dataset and desired performance.
This is a basic example and can be expanded to identify different fighting actions by analyzing other relevant keypoints and joint angles. Additionally, you can integrate this code into a real-time application for continuous action recognition.
python how to best go about this for my project goal and all my extracted features Feature Engineering:
Select and pre-process extracted features based on their relevance to specific fighting actions. Normalize and segment data for efficient training. Consider dimensionality reduction techniques to optimize feature space. View other drafts
Feature Engineering for Real-Time Fighting Action Recognition with Python Here's a breakdown of how to approach feature engineering for your real-time fighting action recognition project in Python:
- Feature Selection:
Categorize features: Group features based on their type (e.g., joint coordinates, velocities, angles, etc.) and their relationship to specific body parts (e.g., legs, arms, torso). Identify action-specific features: Analyze each fighting action and determine which features are most relevant for its recognition. For example, knee and foot angles might be crucial for kicks, while hand and arm velocities might be important for punches. Evaluate feature importance: Implement techniques like statistical tests (e.g., ANOVA) or information gain to quantify the relevance of each feature for different action classifications. 2. Feature Pre-processing:
Missing values: Impute missing values based on statistical methods like mean or median imputation, or utilize interpolation techniques. Normalization: Scale features to a common range (e.g., z-score or min-max normalization) to improve training efficiency and avoid bias. Smoothing: Apply filters (e.g., Savitzky-Golay) to remove noise and jitter in joint trajectories. Feature transformation: Consider techniques like Principal Component Analysis (PCA) to project features onto a lower-dimensional space while preserving essential information. 3. Data Segmentation:
Action segmentation: Divide data into segments corresponding to individual fighting actions. This can be achieved using techniques like rule-based approaches based on specific joint movements or machine learning models trained on labeled data. Overlap management: Decide how to handle overlapping actions within data segments. This can involve assigning a single dominant action label to the segment or creating multiple sub-segments for each individual action. 4. Dimensionality Reduction (optional):
If your feature space is large, consider dimensionality reduction techniques like PCA, Linear Discriminant Analysis (LDA), or autoencoders to reduce the number of features while retaining informative information. This can significantly improve training speed and efficiency, especially for real-time applications. Here are some additional Python libraries and tools that can be helpful for your project:
Scikit-learn: Provides various tools for data analysis, feature engineering, and dimensionality reduction. NumPy and pandas: Offer efficient data structures and manipulation tools for numerical data. OpenCV: Provides libraries for computer vision tasks like optical flow and skeletonization. TensorFlow or PyTorch: Essential libraries for building and deploying deep learning models. Remember:
Experiment with different feature combinations and pre-processing techniques to find what works best for your specific fighting actions and dataset. Evaluate the impact of different feature engineering steps on the accuracy and performance of your action recognition model. Continuously monitor and improve your feature engineering approach based on the results and limitations observed during training and real-world testing. By implementing these techniques and leveraging Python libraries, you can effectively engineer features from Mediapipe pose landmarks to build a robust and efficient real-time fighting action recognition system.
python how to best go about feature engineering for my project goal "Develop a real-time fighting action recognition system that utilizes Mediapipe for pose estimation to extract landmark features from input frames captured by a camera. The system should accurately classify and identify various fighting actions, including low kicks, back kicks, roundhouse kicks, dodges, and other relevant actions. The focus is on creating a robust and efficient solution capable of real-time action recognition within a fighting context, leveraging the pose estimation capabilities of the Mediapipe library." and all my extracted features(Joint coordinates:
3D coordinates of all 33 Mediapipe keypoints. Velocity and acceleration of joint coordinates. Relative coordinates between specific joints (e.g., elbow-wrist angle). Joint distances (e.g., left-right shoulder distance). Motion features:
Joint angles and their derivatives over time (e.g., knee angle change during a kick). Joint velocities and accelerations over time. Joint angular velocities and accelerations over time. Segmental angular velocities and accelerations (e.g., upper arm angular velocity during a punch). Spatial and temporal gradients of joint coordinates and angles. Pose statistics:
Center of mass (COM) trajectory and its derivatives. Body orientation and its derivatives (e.g., yaw, pitch, roll). Extremity motion statistics (e.g., average hand velocity during a block). Joint motion entropy and complexity measures. Action-specific features:
Specific joint trajectories for key fighting actions (e.g., foot trajectory during a low kick). Action-specific joint angles and their derivatives (e.g., knee angle during a roundhouse kick). Time-to-contact (TTC) estimation for specific body parts (e.g., time for a kick to reach the target). Action phase recognition (e.g., identifying preparation, execution, and recovery phases of a punch).) in mind View other drafts
Feature Engineering for Real-Time Fighting Action Recognition Here's how to approach feature engineering for your real-time fighting action recognition project while leveraging Mediapipe and keeping both accuracy and efficiency in mind:
- Data Preprocessing:
Normalization: Normalize all features to a common range to ensure equal weighting during training. Consider techniques like min-max scaling or z-score normalization. Filtering: Apply smoothing filters (e.g., Kalman filter) to remove noise from joint trajectories and improve feature stability. Segmentation: Segment data into action sequences of fixed length (e.g., 1-2 seconds) for efficient model training and prediction. 2. Feature Selection:
Relevance: Select features that are most relevant to specific fighting actions. Analyze the discriminative power of features based on their correlation with action labels. Redundancy: Eliminate redundant features that are highly correlated with others to avoid overfitting and improve training efficiency. Dimensionality reduction: Consider dimensionality reduction techniques like PCA or feature selection algorithms to reduce feature space dimensionality and improve computational efficiency. 3. Action-Specific Features:
Joint trajectory features: Extract specific joint trajectories for key fighting actions (e.g., foot trajectory for a low kick). Action-specific angles and derivatives: Calculate joint angles and their derivatives relevant to specific actions (e.g., knee angle in a roundhouse kick). Time-to-contact (TTC): Estimate the time for specific body parts to reach a target, valuable for pre-emptive action recognition. Action phase recognition: Develop algorithms to identify preparation, execution, and recovery phases of different actions for more precise classification. 4. Feature Engineering Techniques:
Statistical features: Calculate statistics like mean, standard deviation, and skewness of joint coordinates, velocities, and angles to capture overall motion patterns. Frequency domain features: Apply Fast Fourier Transform (FFT) to extract frequency-domain information from joint trajectories and angles. Optical flow features: Calculate optical flow between consecutive frames to capture overall motion patterns and directionality. Skeletonization features: Represent the pose as a stick figure, reducing complexity while retaining key motion information. 5. Real-time Efficiency Considerations:
Feature selection: Prioritize features that can be efficiently calculated in real-time with limited computational resources. Lightweight models: Choose deep learning architectures optimized for real-time inference on embedded devices or GPUs. Quantization: Quantize model weights and activations to lower precision formats (e.g., int8) for faster inference and reduced memory footprint. Model pruning: Remove redundant or unimportant parts of the trained model to reduce model size and inference time. 6. Evaluation and Iteration:
Evaluate the performance of different feature sets and engineering techniques on a dedicated validation set. Analyze feature importance and impact on classification accuracy to identify the most effective features for your specific actions and context. Continuously iterate and refine your feature engineering process based on performance metrics and new insights. Tools and Resources:
Python libraries: NumPy, Pandas, Scikit-learn, OpenCV Deep learning frameworks: TensorFlow, PyTorch Mediapipe library: Pose estimation and landmark extraction Visualization tools: Matplotlib, Seaborn Hardware: GPU for efficient training and inference By carefully applying these recommendations and leveraging the available tools and resources, you can design an effective and efficient feature engineering approach for your real-time fighting action recognition system. Remember to experiment with different techniques, evaluate your results, and continuously improve your approach to achieve the best possible performance within your specific context and constraints.
what is optical flow and how can i use it in my project goal View other drafts
Optical Flow and its Potential Use in your Fighting Action Recognition Project What is Optical Flow?
Optical flow refers to the apparent motion of patterns of brightness, color, or texture in an image sequence caused by the relative movement between an observer (e.g., a camera) and a scene. It can be visualized as a vector field, where each vector represents the displacement of a pixel between consecutive frames.
Applications of Optical Flow:
Motion analysis: Measuring the movement of objects in a scene, such as tracking the movement of limbs during a fighting action. Object segmentation: Separating objects from the background based on their motion. Video compression: Exploiting redundancies in the motion information between frames to reduce storage requirements. Robot navigation: Estimating the ego-motion of a robot by analyzing the optical flow of the surrounding environment. Potential Use in your Fighting Action Recognition Project:
Optical flow can be a valuable tool for your fighting action recognition project in several ways:
Supplementing landmark features: Combining optical flow information with the extracted landmark features from Mediapipe can provide a more comprehensive description of the scene and potentially improve the accuracy of action classification. Capturing global motion patterns: Optical flow can capture global motion patterns that might not be easily captured by individual landmark features, such as the overall flow of the body during a punch or kick. Identifying subtle movements: Optical flow can be sensitive to subtle changes in motion, which can be helpful for identifying specific fighting actions that involve small movements, such as dodges or feints. Here's how you can integrate optical flow into your project:
Extract optical flow features: Use an optical flow algorithm, such as Lucas-Kanade or Farneback, to compute the optical flow field between consecutive video frames. Represent the flow field: Represent the optical flow information in a way that is useful for your action classification model. For example, you can calculate the average magnitude and direction of the flow field, or extract histograms of the flow vectors. Combine with landmark features: Combine the extracted optical flow features with your existing landmark features to create a more comprehensive feature representation. Train your model: Train your deep learning model on the combined feature set to improve the accuracy of action recognition. Resources for Implementing Optical Flow:
OpenCV library: Provides efficient implementations of various optical flow algorithms. TensorFlow Addons: Offers a pre-trained optical flow model that can be easily integrated into your project. PyTorch Lightning: Provides a framework for training and deploying deep learning models with ease. By incorporating optical flow into your fighting action recognition system, you can potentially achieve higher accuracy and gain valuable insights into the dynamics of different fighting actions.
Prioritizing Features for Real-Time Fighting Action Recognition with Mediapipe: Given your project goal of real-time fighting action recognition using Mediapipe, here's a possible prioritization of the listed features:
High Importance:
Joint coordinates: 3D coordinates of all 33 Mediapipe keypoints are crucial for capturing body posture and movement. Joint angles and their derivatives: These features provide information about limb rotations and velocities, vital for recognizing specific actions. Segmental angular velocities and accelerations: These features capture the relative motion of different body segments, further aiding in action identification. Pose statistics: Center of mass (COM) trajectory and its derivatives offer insights into overall body motion and can be useful for differentiating complex actions. Action-specific joint trajectories: Extracting key joint trajectories for specific fighting actions (e.g., foot trajectory for kicks) directly captures the characteristic motion patterns. Medium Importance:
Relative coordinates between specific joints: These features provide additional information about joint relationships and can be helpful for certain actions. Joint velocities and accelerations: While not as essential as angles, these features can still contribute to recognizing actions based on speed and impact. Extremity motion statistics: Average hand velocity during a block or average foot speed during footwork can be beneficial for specific action detection. Time-to-contact (TTC) estimation: This feature can be valuable for anticipating the timing of strikes and blocks but might not be crucial for all actions. Low Importance:
Joint distances: These features can be helpful for specific actions (e.g., determining stance width), but generally contribute less than joint coordinates or angles. Spatial and temporal gradients: While potentially informative, these features might add unnecessary complexity for real-time performance. Joint motion entropy and complexity measures: These features might offer insights into movement patterns but are likely less relevant for action recognition compared to other features. Action phase recognition: While helpful for detailed analysis, identifying individual phases (preparation, execution, recovery) might not be crucial for real-time action classification. Additional features: Optical flow, skeletonization, silhouette, and action context might be beneficial in specific scenarios but likely add significant processing overhead, impacting real-time performance. Remember:
This prioritization is a suggestion and may need adjustments based on your specific project requirements and available resources. Feature selection should be done based on a balance between accuracy, real-time performance, and computational cost. Prioritizing features and optimizing code are crucial for achieving real-time fighting action recognition with Mediapipe. Experimentation and evaluation are key:
Train and test your model with different feature combinations to evaluate their impact on accuracy and performance. Continuously refine your feature selection and model architecture to achieve the best results for your specific project goals. By carefully choosing and prioritizing features, you can develop a robust and efficient real-time fighting action recognition system using Mediapipe.