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Yolov5 + Deep Sort with PyTorch

forked from {https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch} and made changes based on our own dataset.

Introduction

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.

Before you run the tracker

  1. Clone the repository:

git clone https://github.com/unmannedlab/UWB_Dataset.git

  1. 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 ~/UWB_Dataset/benchmarks/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

Run tracker and evaluate on our own dataset while following MOT16 format

Generate tracking results for each sequence in our dataset

python3 track.py --source path_to_output.mp4 --save-txt --save-vid --evaluate --yolo_model yolov5/weights/crowdhuman_yolov5m.pt --classes 0 --imgsz 728 1296

Note: remember to change the path to output.mp4 video, for example ~/Document/output.mp4

Moving data from experiment folder to MOT16 folder

mv ~/UWB_Dataset/benchmarks/runs/track/exp/* \
   ~/benchmarks/MOT16_eval/TrackEval/data/trackers/mot_challenge/MOT16-train/ch_yolov5m_deep_sort/data/

Run the evaluation on our dataset

python ~/UWB_Dataset/benchmarks/MOT16_eval/TrackEval/scripts/run_mot_challenge.py --BENCHMARK MOT16 \
 --TRACKERS_TO_EVAL ch_yolov5m_deep_sort --SPLIT_TO_EVAL train --METRICS CLEAR Identity HOTA \
 --USE_PARALLEL False --NUM_PARALLEL_CORES 4

Cite

If you find this repo useful in your research, please also 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}
}