This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and Waymo datasets. Addressing the gap in existing tracking paradigms, which often perform well on specific datasets but lack generalizability, MCTrack offers a unified solution. Additionally, we have standardized the format of perceptual results across various datasets, termed BaseVersion, facilitating researchers in the field of multi-object tracking (MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we propose a novel set that assesses motion information output, such as velocity and acceleration, crucial for downstream tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack}{https://github.com/megvii-research/MCTrack
@article{arxiv.2409.16149,
title = {MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving},
author = {Xiyang Wang and Shouzheng Qi and Jieyou Zhao and Hangning Zhou and Siyu Zhang and Guoan Wang and Kai Tu and Songlin Guo and Jianbo Zhao and Jian Li and Mu Yang},
journal= {arXiv preprint arXiv:2409.16149},
year = {2024}
}