This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algorithm which tracks the objects. It can track any object that your Yolov5 model was trained to detect.
- Yolov5 training on Custom Data (link to external repository)
- Deep Sort deep descriptor training (link to external repository)
- Yolov5 deep_sort pytorch evaluation
- Clone the repository recursively:
git clone --recurse-submodules https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch.git
If you already cloned and forgot to use --recurse-submodules you can run git submodule update --init
- Make sure that you fulfill all the requirements: Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install, run:
pip install -r requirements.txt
Tracking can be run on most video formats
python3 track.py --source ... --show-vid # show live inference results as well- Video:
--source file.mp4 - Webcam:
--source 0 - RTSP stream:
--source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa - HTTP stream:
--source http://wmccpinetop.axiscam.net/mjpg/video.mjpg
There is a clear trade-off between model inference speed and accuracy. In order to make it possible to fulfill your inference speed/accuracy needs you can select a Yolov5 family model for automatic download
$ python track.py --source 0 --yolo_weights yolov5s.pt --img 640
yolov5m.pt
yolov5l.pt
yolov5x.pt --img 1280If you only want to track persons I recommend you to get these weights for increased performance
python3 track.py --source 0 --yolo_weights yolov5/weights/crowdhuman_yolov5m.pt --classes 0 # tracks persons, onlyIf you want to track a subset of the MS COCO classes, add their corresponding index after the classes flag
python3 track.py --source 0 --yolo_weights yolov5s.pt --classes 0