yolov5 deepsort pytorch

real time object detection and tracking

usage:

# set up env
poetry env use python3.7
poetry install
# run tracking with preview; if model doesn't exist it'll be downloaded
poetry run yolo_track --source test/ducks1.mp4 --yolo_model models/yolov5s6.pt --show-vid
# run tracking and stream output to file
poetry run yolo_track --source test/ducks1.mp4 --yolo_model models/yolov5s6.pt --save-txt --out-txt /tmp/track1.txt
# run tracking and stream output to rabbitmq
poetry run yolo_track --source ./test/ducks1.mp4 --yolo_model models/yolov5m6.pt --log-rmq --out-rmq example.com:5671,yolo1,user:pass
# youtube livestream
poetry run yolo_track --source "$(youtube-dl -f 'bestvideo[height<=480]+bestaudio/best[height<=480]' -g 'https://www.youtube.com/watch?v=JJqXeRFsLjE')" --yolo_model models/yolov5m6.pt --save-txt --out-txt /tmp/obj1.txt
# youtube livestream with yt-dlp
poetry run yolo_track --source "$(yt-dlp -f 'bestvideo[height<=480]+bestaudio/best[height<=480]' -g 'https://www.youtube.com/watch?v=s4SiFUNYdTs' | head -n 1)" --yolo_model models/yolov5n6.pt --save-txt --out-txt /tmp/obj1.txt

within docker:

# basic test
mkdir -p /tmp/yolo && echo -n > /tmp/yolo/obj1.txt && docker run -v /tmp/yolo:/out -it --rm yolo_track:dev yolo_track.track --source 'https://some-website.domain/some_stream.ts' --yolo_model models/yolov5n6.pt --save-txt --out-txt /out/obj1.txt --frames 10
# live tracking and data streaming
podman run -it --rm -v $(pwd)/certs:/certs xdrie/yolo_track:v0.6 yolo_track.track --source <stream_url> --yolo_model models/yolov5n6.pt --deep_sort_model osnet_ain_x0_5 --log-rmq --out-rmq <rmq_connstr>

to get more models, just

Yolov5 + Deep Sort with PyTorch

Introduction

This repository contains a highly configurable two-stage-tracker that adjusts to different deployment scenarios. 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.

Tutorials

• Yolov5 training on Custom Data (link to external repository) 
• DeepSort deep descriptor training (link to external repository) 
• Yolov5 deep_sort pytorch evaluation 

Before you run the tracker

1. 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

2. 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 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

Select object detection and ReID model

Yolov5

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_model yolov5n.pt --img 640
                                          yolov5s.pt
                                          yolov5m.pt
                                          yolov5l.pt 
                                          yolov5x.pt --img 1280
                                          ...

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
                                               ...

Filter tracked classes

By default the tracker tracks all MS COCO classes.

If you only want to track persons I recommend you to get these weights for increased performance

python3 track.py --source 0 --yolo_model yolov5/weights/crowdhuman_yolov5m.pt --classes 0  # tracks persons, only

If 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_model yolov5s.pt --classes 16 17  # tracks cats and dogs, only

Here is a list of all the possible objects that a Yolov5 model trained on MS COCO can detect. Notice that the indexing for the classes in this repo starts at zero.

MOT compliant results

Can be saved to your experiment folder track/expN by

python3 track.py --source ... --save-txt

Cite

If you find this project useful in your research, please consider cite:

@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}
}