🏍️ 🚙 Vehicle Tracking using YOLOv5 + DeepSORT 🚌

UPDATE 16.07.2021 - BIG REFACTOR

Code is cleaned and working fine now, promise 🤞

Method

• Use YOLOv5 or EfficientDet for vehicle detection task, only considers objects in Region of Interest (ROI)
• Use DeepSORT for car tracking, not need to retrain this model, only inference
• Use Cosine Similarity to assign object's tracks to most similar directions.
• Count each type of vehicle on each direction.

📔 Notebook

• For inference, use this notebook
• To retrain detection model, follow instructions from my template

Results

---

Dataset

• AIC-HCMC-2020: link
• Direction and ROI annotation format:

cam_01.json # match video name
{
    "shapes": [
        {
            "label": "zone",
            "points": [[x1,y1], [x2,y2], [x3,y3], [x4,y4], ... ] #Points of a polygon
        },
        {
            "label": "direction01",
            "points": [[x1,y1], [x2,y2]] #Points of vector
        },
        {
            "label": "direction{id}",
            "points": [[x1,y1], [x2,y2]]
        },...
    ],
}

🥇 Pretrained weights

• Download finetuned models from on AIC-HCMC-2020 dataset:

Model	Image Size	Weights	MAP-S	MAP-M	MAP-L	ALL
EfficientDet-D2	640x640	link	0.061	0.456	0.475	0.451
YOLOv5s	640x640	link	0.17	0.466	0.487	0.466
YOLOv5m	640x640	link	0.187	0.487	0.501	0.481

🌟 Inference

• File structure

this repo
│   detect.py
└───configs
│      configs.yaml           # Contains model's configurations
│      cam_configs.yaml       # Contains DEEPSORT's configuration for each video

• Install dependencies by pip install -r requirements.txt
• To run full pipeline:

python run.py --input_path=<input video or dir> --output_path=<output dir> --weight=<trained weight>

• Extra Parameters:
  • --min_conf: minimum confident for detection
  • --min_iou: minimum iou for detection

References

• DeepSORT from https://github.com/ZQPei/deep_sort_pytorch
• YOLOv5 from https://github.com/ultralytics/yolov5
• Train YOLOv5 using https://github.com/kaylode/custom-template/tree/detection