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Multiple Object Tracking as ID Prediction

Ruopeng Gao,  Ji Qi,  Limin Wang
Nanjing University
📧 Primary Contact: ruopenggao@gmail.com

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🔍 Overview

TL; DR. We propose a novel perspective to regard the multiple object tracking task as an in-context ID prediction problem. Given a set of trajectories carried with ID information, MOTIP directly decodes the ID labels for current detections, which is straightforward and effective.

Overview

🔥 News

  • 2025.07.10: Thanks everyone—we’ve hit 300 stars 🎉! A new TUTORIAL (is updating gradually) is live to help understand and work with the model.
  • 2025.04.11: Support loading previous MOTIP checkpoints from the prev-engine to inference 💾. See MODEL_ZOO for details.
  • 2025.04.06: Now, you can use the video demo to perform nearly real-time tracking on your videos 🕹️.
  • 2025.04.05: We support FP16 for faster inference 🏎️.
  • 2025.04.03: The new codebase is released 🎉. Compared to the previous version, it is more concise and efficient 🚀. Feel free to enjoy it!
  • 2025.03.25: Our revised paper is released at arXiv:2403.16848. The latest codebase is being organized 🚧.
  • 2025.02.27: Our paper is accepted by CVPR 2025 🎉 🎉. The revised paper and a more efficient codebase will be released in March. Almost there 🤓 ~
  • 2024.03.26: The first version of our paper is released at arXiv:2403.16848v1 📌. The corresponding codebase is stored in the prev-engine branch (No longer maintained starting April 2025 ⛔).

💨 Quick Start

  • See INSTALL.md for instructions of installing required components.
  • See DATASET.md for datasets download and preparation.
  • See GET_STARTED.md for how to get started with our MOTIP, including pre-training, training, and inference.
  • See MODEL_ZOO.md for well-trained models.
  • See MISCELLANEOUS.md for other miscellaneous settings unrelated to the model structure, such as logging.
  • See TUTORIAL.md to understand and better develop our models.

💐 Acknowledgements

This project is built upon Deformable DETR, MOTR, TrackEval. Thanks to the contributors of these great codebases.

✏️ Citation

If you think this project is helpful, please feel free to leave a ⭐ and cite our paper:

@InProceedings{{MOTIP},
    author    = {Gao, Ruopeng and Qi, Ji and Wang, Limin},
    title     = {Multiple Object Tracking as ID Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27883-27893}
}

🌟 Stars

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[CVPR 2025] Multiple Object Tracking as ID Prediction

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  • Python 90.2%
  • Cuda 6.0%
  • Shell 1.9%
  • HTML 1.3%
  • C++ 0.6%