LEGO: Learning and Graph-Optimized Modular Tracker for Online
  Multi-Object Tracking with Point Clouds
Zhenrong Zhang Jianan Liu Yuxuan Xia Tao Huang Qing-Long Han Hongbin Liu
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