Yolov5 + DeepSort

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 the tracker of your choice. Supported ones at the moment are: StrongSORT OSNet and OCSORT. They can track any object that your Yolov5 model was trained to detect.

Before you run the tracker

1. Clone the repository recursively:

git clone --recurse-submodules https://github.com/ssohug/Yolov5_StrongSORT_OSNet.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 methods

$ python track.py --tracking-method strongsort
                                    ocsort

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-weights yolov5n.pt --img 640
                                            yolov5s.tflite
                                            yolov5m.pt
                                            yolov5l.onnx 
                                            yolov5x.pt --img 1280
                                            ...

StrongSORT

The above applies to StrongSORT models as well. Choose a ReID model based on your needs from this ReID model zoo

$ python track.py --source 0 --strong-sort-weights osnet_x0_25_market1501.pt
                                                   mobilenetv2_x1_4_msmt17.engine
                                                   resnet50_msmt17.onnx
                                                   osnet_x1_0_msmt17.pt
                                                   ...

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

python track.py --source 0 --yolo-weights 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

python track.py --source 0 --yolo-weights 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 runs/track/<yolo_model>_<deep_sort_model>/ by

python track.py --source ... --save-txt

Cite

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

@misc{yolov5-strongsort-osnet-2022,
    title={Real-time multi-camera multi-object tracker using YOLOv5 and DeepSort},
    author={Md. Saiful Islam},
    howpublished = {\url{https://github.com/ssohug/YOLOv5-line.git}},
    year={2022}
}

Contact

For Yolov5 DeepSort OSNet bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please send an email to: yolov5.deepsort.pytorch@gmail.com