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

This paper introduces MCTrack, a new 3D multi-object tracking method that achieves 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 MOT) to concentrate on the core algorithmic development without the undue burden of data preprocessing. Finally, recognizing the limitations of current evaluation metrics, we introduce a novel set of metrics designed to evaluate the output of motion information, including velocity and acceleration, which are essential for subsequent tasks. The source codes of the proposed method are available at this link: https://github.com/megvii-research/MCTrack