MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
<|reference_start|>MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving: 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<|reference_end|>
@article{wang2024mctrack:,
    title={MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous
  Driving},
    author={Xiyang Wang, Shouzheng Qi, Jieyou Zhao, Hangning Zhou, Siyu Zhang,
  Guoan Wang, Kai Tu, Songlin Guo, Jianbo Zhao, Jian Li, Mu Yang},
    journal={arXiv preprint arXiv:2409.16149},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16149},
    primaryClass={cs.CV}
}