Online multi-object tracking (MOT) plays a pivotal role in autonomous systems. The state-of-the-art approaches usually employ a tracking-by-detection method, and data association plays a critical role. This paper proposes a learning and graph-optimized (LEGO) modular tracker to improve data association performance in the existing literature. The proposed LEGO tracker integrates graph optimization and self-attention mechanisms, which efficiently formulate the association score map, facilitating the accurate and efficient matching of objects across time frames. To further enhance the state update process, the Kalman filter is added to ensure consistent tracking by incorporating temporal coherence in the object states. Our proposed method utilizing LiDAR alone has shown exceptional performance compared to other online tracking approaches, including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 1st at the time of submitting results to KITTI object tracking evaluation ranking board and remains 2nd at the time of submitting this paper, among all online trackers in the KITTI MOT benchmark for cars1
@article{arxiv.2308.09908,
title = {LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds},
author = {Zhenrong Zhang and Jianan Liu and Yuxuan Xia and Tao Huang and Qing-Long Han and Hongbin Liu},
journal= {arXiv preprint arXiv:2308.09908},
year = {2025}
}
Comments
Accept by IEEE Transactions on Circuits and Systems for Video Technology