This is the official PyTorch implementation of our paper:
History-Aware Transformation of ReID Features for Multiple Object Tracking
🎓 Ruopeng Gao, Yuyao Wang, Chunxu Liu, Limin Wang
📧 Primary contact: ruopenggao@gmail.com
TL; DR. We propose a plug-and-play History-Aware Transformation algorithm for appearance features (i.e., ReID features). Guided by historical information, we seek a more discriminative subspace for target features in video sequences, enabling better differentiation between different trajectories. Our method significantly enhances the reliability of appearance features, improving the performance of ReID-based MOT trackers.
- 2026.07.22: The latest version of our paper is now available on arXiv. It includes a more systematic analysis of the relationship between similarity measures and tracking performance, revealing long-standing issues in the ReID space and explaining how our method improves its representations. 🔥
- 2026.07.18: The updated version of the paper will be posted on arXiv soon, with a more comprehensive analysis. 🔜
- 2026.06.18: Our paper is accepted by ECCV 2026 ~ 🎉 🎉
Theoretically, our proposed method can be applied to any multiple object tracker equipped with a ReID or appearance branch. Since different methods are often built on varying codebases, our repository is organized into separate sub-projects to ensure compatibility with different frameworks. Each sub-project should be treated and run independently.
Currently, we provide the following sub-project implementations:
- HAT-MASA: MASA is a tracking model based purely on appearance feature matching, which has achieved remarkable performance in zero-shot tracking.
- HAT-SORT: Since nearly all heuristic Multiple Object Tracking (MOT) algorithms adhere to the SORT-like design paradigm, we have selected a set of the most popular codebases as our foundation for implementation and modification.
If you think this project is helpful, please feel free to leave a ⭐ and cite our paper:
@article{{HATReID-MOT},
title={History-aware transformation of reid features for multiple object tracking},
author={Gao, Ruopeng and Wang, Yuyao and Liu, Chunxu and Wang, Limin},
journal={arXiv preprint arXiv:2503.12562},
year={2025}
}