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

本文提出了一种名为 LEGO 的模块化跟踪器，通过集成图优化和自注意机制来改善现有文献中的数据关联性能，进一步提高状态更新过程，并且在 KITTI MOT 基准测试中表现出优异的性能。