This is the official PyTorch implementation of our paper:
Multiple Object Tracking as ID Prediction
🎓 Ruopeng Gao, Ji Qi, Limin Wang
📧 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.
- 2025.04.03: 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 The corresponding codebase is stored in the prev-engine branch (No longer maintained starting April 2025 ⛔).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:
@article{MOTIP,
title={Multiple Object Tracking as ID Prediction},
author={Gao, Ruopeng and Qi, Ji and Wang, Limin},
journal={arXiv preprint arXiv:2403.16848},
year={2024}
}