Visual Multi-object Tracking Algorithms

This is the official Python implementation repository for a paper entitled "Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking", ICCAIS 2023 (arXiv 2312.01650).

- BYTETrack
- OC-SORT
- FairMOT
- SORT
- CMC (Camera Motion Compensation) 
- HOTA Evaluation

Usage

1. Set Up Python Environment

   • Create a conda Python environment and activate it:

     conda create --name virtualenv python==3.8.0
     conda activate virtualenv

   • lone this repository recursively to have pybind11

     git clone --recursive https://github.com/linh-gist/AdaptConfByteTrack.git

   • Install Packages

     numpy==1.23.1
     opencv-python==4.9.0.80
     loguru==0.7.2
     scipy==1.10.1
     lap==0.5.12
     cython_bbox==0.1.5
     matplotlib==3.5.3
     filterpy==1.4.5
     motmetrics==1.4.0
     openpyxl==3.1.5
     pycocotools==2.0.7
     tabulate==0.9.0
     # git clone https://github.com/JonathonLuiten/TrackEval.git
     # cd TrackEval, python setup.py build develop

2. Prepare Data

   • Datasets:
     • MOT16, MOT17, MOT20
     • You can also run with your custom dataset but need a detector

3. Run the Tracking Demo

   • Change parameters in make_parser() in track.py such as use_gmc, data_dir (MOTChallenge GT data)
   • Run python track.py

Citation

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

@inproceedings{van2023adaptive,
  title={Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking},
  author={Linh Van Ma and Muhammad Ishfaq Hussain and JongHyun Park and Jeongbae Kim and Moongu Jeon},
  booktitle={2023 12th International Conference on Control, Automation and Information Sciences (ICCAIS)},
  pages={370--374},
  year={2023},
  organization={IEEE}
}

Acknowledgement

A part of the code is borrowed from ByteTrack, OC-SORT, BoT-SORT, FairMOT and SORT. Thanks for their wonderful works.