This repository contains a moded version of PyTorch YOLOv3 (https://github.com/ultralytics/yolov3). It filters out every detection that is not a person. The detections of persons are then passed to a Deep Sort algorithm (https://github.com/ZQPei/deep_sort_pytorch) which tracks the persons. The reason behind the fact that it just tracks persons is that the deep association metric is trained on a person ONLY datatset.
The implementation is based on two papers:
- Simple Online and Realtime Tracking with a Deep Association Metric https://arxiv.org/abs/1703.07402
- YOLOv3: An Incremental Improvement https://arxiv.org/abs/1804.02767
Python 3.7 or later with all of the pip install -U -r requirements.txt packages including:
torch >= 1.3opencv-pythonPillow
All dependencies are included in the associated docker images. Docker requirements are:
nvidia-docker- Nvidia Driver Version >= 440.44
Github block pushes of files larger than 100 MB (https://help.github.com/en/github/managing-large-files/conditions-for-large-files). Hence the yolo weights needs to be stored somewhere else. When you run tracker.py you will get an exceptions telling you that the yolov3 weight are missing and a link to download them from. Place the downlaoded .pt file under yolov3/weights/. The weights for deep sort are already in this repo. They can be found under deep_sort/deep/checkpoint/.
track.py runs tracking on any video source:
python3 track.py --source ...- 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
For more detailed information about the algorithms and their corresponding lisences used in this project access their official github implementations.