With the widespread adoption of camera and LiDAR sensors, 3D multi-object tracking (MOT) technology has been extensively applied across numerous fields such as robotics, autonomous driving, and surveillance. However, existing 3D MOT methods still face significant challenges in addressing issues such as false detections, ghost trajectories, incorrect associations, and identity switches. To address these challenges, we propose a lightweight 3D multi-object tracking framework via collaborative camera and LiDAR sensors. Firstly, we design a confidence inverse normalization guided ghost trajectories suppression module (CIGTS). This module suppresses false detections and ghost trajectories at their source using inverse normalization and a virtual trajectory survival frame strategy. Secondly, an adaptive matching space-driven lightweight association module (AMSLA) is proposed. By discarding global association strategies, this module improves association efficiency and accuracy using low-cost decision factors. Finally, a multi-factor collaborative perception-based intelligent trajectory management module (MFCTM) is constructed. This module enables accurate retention or deletion decisions for unmatched trajectories, thereby reducing computational overhead and the risk of identity mismatches. Extensive experiments on the KITTI dataset show that the proposed method outperforms state-of-the-art methods across multiple performance metrics, achieving Higher Order Tracking Accuracy (HOTA) scores of 80.13% and 53.24% for the Car and Pedestrian categories, respectively.
Feng et al. (Wed,) studied this question.