COS40005 - Computing Technology Project A

Group 4

• Nguyen Cuong Nhat
• Vuong Khang Minh
• Nguyen Dang Duc Anh
• Nguyen Ha Huy Hoang
• Nguyen Dang Khanh Toan

Description

Run human tracking and object detection using YOLOv10 and DeepSORT

Installation

1. Clone this repository:

 git clone https://github.com/nhat120904/Human-tracking-project-using-YOLOv10-and-DeepSORT.git
 cd YOLOv10_DeepSORT

2. Create new environment

• Using Conda

conda env create -f conda.yml
conda activate yolov10-deepsort

• Using pip

python3 -m virtualenv -p python3.11 yolov10-deepsort
source yolov10-deepsort/bin/activate
pip install -r requirements.txt

3. Download model weight

 mkdir -p weights
 wget -P weights -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10n.pt
 wget -P weights -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10s.pt
 wget -P weights -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10m.pt
 wget -P weights -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10b.pt
 wget -P weights -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10x.pt
 wget -P weights -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10l.pt
 ls -lh weights

Usage

1. Prepare the video file:

   • Place the video file in the desired location.
   • Update the video flag in the path of the video file or set it to 0 to use the webcam as the input.

2. Download YOLOv10 model:

   • Make sure the corresponding model weights are available.(yolov10n/yolov10s/yolov10m/yolov10b/yolov10x/yolov10l)

3. Configure the output video:

   • Update output flag in the code to specify the path and filename of the output video file.

4. Set the confidence threshold:

   • Adjust the conf flag in the code to set the confidence threshold for object detection. Objects with confidence below this threshold will be filtered out.

5. If you want to detect and track certain object on video

   • Modify the class_id flag in the code to specify the class ID for detection. The default value of the flag is set to None. If you wish to detect and track only persons, set it to 0, or refer to the coco.names file for other options.

6. If you want to blur certain object while tracking

   • Modify the bulr_id flag in the code to specify the class ID for detection. The default value of the flag is set to None.

7. Run the code:

   # Run object tracking
   python object_tracking.py --video ./data/test.mp4 --output ./output/output.mp4
   
   # Run object tracking on webcam (set video flag to 0)
   python object_tracking.py --video 0 --output ./output/webcam.mp4
   
   # Run person tracking (set class_id flag to 0 for person)
   python object_tracking.py --video ./data/test.mp4 --output ./output/output.mp4 --class_id 0
   
   # Run tracking on a video with burring certain objects (set blur_id flag to 0 for person)
   python object_tracking.py --video ./data/test.mp4 --output ./output/output.mp4 --blur_id 0
   

8. Inference app

   # Install Streamlit
   pip install streamlit
   
   # Run Streamlit app
   streamlit run app.py
   

Acknowledgements

• This code is built upon the YOLOv10 model and the DeepSort algorithm.
• Credits to the authors and contributors of the respective repositories used in this project.

References

• YOLOv10: Real-Time End-to-End Object Detection
• Simple Online and Realtime Tracking with a Deep Association Metric