An example of using DanceTrack.

This code is based on ByteTrack. We provide the trained models in Google Drive or Baidu Drive(code:awew).

• Follow installation steps in ByteTrack. Replace the original files with updated ones here.

• Prepare DanceTrack dataset as in Dataset.

• Convert annotations to coco format:

cd {DanceTrack ROOT}
python3 tools/convert_dance_to_coco.py
cd ByteTrack/datasets
ln -s ../../dancetrack dancetrack
cd ..

The COCO pretrained YOLOX model can be downloaded from their model zoo. After downloading the pretrained models, put them under {DanceTrack ROOT}/ByteTrack/pretrained.

cd {DanceTrack ROOT}/ByteTrack
python3 tools/train.py -f exps/example/dancetrack/yolox_x.py -d 8 -b 48 --fp16 -o -c pretrained/yolox_x.pth

python3 tools/track.py -f exps/example/dancetrack/yolox_x.py -c YOLOX_outputs/yolox_x/latest_ckpt.pth.tar -b 1 -d 1 --fp16 --fuse

The output txt will be saved in YOLOX_outputs/yolox_x/track_results folder.

Evaluate the results as in Evaluation.

python3 tools/track.py -f exps/example/dancetrack/yolox_x.py -c YOLOX_outputs/yolox_x/latest_ckpt.pth.tar -b 1 -d 1 --fp16 --fuse --test

The output txt will be saved in YOLOX_outputs/yolox_x/track_test_results folder.

Submit the results to CodaLab as in Competition.

If you find this project useful for your research, please use DanceTrack and ByteTrack BibTeX entry.

@article{peize2021dance,
  title   =  {DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion},
  author  =  {Peize Sun and Jinkun Cao and Yi Jiang and Zehuan Yuan and Song Bai and Kris Kitani and Ping Luo},
  journal =  {arXiv preprint arXiv:2111.14690},
  year    =  {2021}
}

@article{zhang2021bytetrack,
  title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
  author={Zhang, Yifu and Sun, Peize and Jiang, Yi and Yu, Dongdong and Yuan, Zehuan and Luo, Ping and Liu, Wenyu and Wang, Xinggang},
  journal={arXiv preprint arXiv:2110.06864},
  year={2021}
}