MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving

• URL: http://arxiv.org/abs/2409.16149v2
• Date: Mon, 14 Oct 2024 16:00:23 GMT
• Title: MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
• Authors: Xiyang Wang, Shouzheng Qi, Jieyou Zhao, Hangning Zhou, Siyu Zhang, Guoan Wang, Kai Tu, Songlin Guo, Jianbo Zhao, Jian Li, Mu Yang,
• Abstract summary: This paper introduces MCTrack, a new 3D multi-object tracking method that achieves state-of-the-art (SOTA) performance across KITTI, nuScenes, and datasets.
• Score: 10.399817864597347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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