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Online multi-object tracking (MOT) plays a pivotal role in autonomoussystems. The state-of-the-art approaches usually employ a tracking-by-detectionmethod, and data association plays a critical role. This paper proposes alearning and graph-optimized (LEGO) modular tracker to improve data associationperformance in the existing literature. The proposed LEGO tracker integratesgraph optimization and self-attention mechanisms, which efficiently formulatethe association score map, facilitating the accurate and efficient matching ofobjects across time frames. To further enhance the state update process, theKalman filter is added to ensure consistent tracking by incorporating temporalcoherence in the object states. Our proposed method utilizing LiDAR alone hasshown exceptional performance compared to other online tracking approaches,including LiDAR-based and LiDAR-camera fusion-based methods. LEGO ranked 1st atthe time of submitting results to KITTI object tracking evaluation rankingboard and remains 2nd at the time of submitting this paper, among all onlinetrackers in the KITTI MOT benchmark for cars1

LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds