The KITTI Vision Benchmark Suite

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

TBD

@misc{wang2024mctrackunified3dmultiobject,
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},
year={2024},
eprint={2409.16149},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2409.16149},
}