Pedestrian Speed Estimation using yolov5, deepsort and homography matrix

Demo gif

Before you run the tracker

1. Clone the repository

2. Install dependencies

pip install -r requirements.txt

Download weights

Download weights and place it in yolov5/weights folder

Quick run

Add a video to data/videos folder and run the following command.

python track.py --source data/videos/test.mp4 --yolo_model yolov5/weights/crowdhuman_yolov5m.pt --classes 0 --show-vid --save-vid

Results are saved to folder track/expN

Tracking sources

Tracking can be run on most video formats

$ python track.py --source 0  # webcam
                           img.jpg  # image
                           vid.mp4  # video
                           path/  # directory
                           path/*.jpg  # glob
                           'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                           'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

DeepSort

Choose a ReID model based on your needs from this ReID model zoo

$ python track.py --source 0 --deep_sort_model osnet_x1_0
                                               nasnsetmobile
                                               resnext101_32x8d
                                               ...

Reference

@misc{yolov5deepsort2020,
    title={Real-time multi-object tracker using YOLOv5 and deep sort},
    author={Mikel Broström},
    howpublished = {\url{https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch}},
    year={2020}
}