MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
It addresses the lack of generalizability in existing tracking methods for autonomous driving, though it appears incremental by building on prior paradigms.
This paper tackles the problem of 3D multi-object tracking for autonomous driving by introducing MCTrack, a unified framework that achieves state-of-the-art performance across KITTI, nuScenes, and Waymo datasets, and also proposes standardized data formats and new evaluation metrics.
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