Ruopeng Gao,
Ji Qi,
Limin Wang,
Nanjing University
📧 Primary Contact: ruopenggao@gmail.com
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.
- 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 The corresponding codebase is stored in the prev-engine branch (No longer maintained starting April 2025 ⛔).
- 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.
This project is built upon Deformable DETR, MOTR, TrackEval. Thanks to the contributors of these great codebases.
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}
}