Multi-target Tracking with Occlusion Resistance for Mobile Robots in Dynamic Environments^*

In the context of tracking multiple targets on a novel mobile robot, it is essential to obtain the three-dimensional coordinates of specified targets based on tracking boxes. Most existing multi-target tracking algorithms neglect the inherent constraints of the novel mobile robot, such as insufficient computational power, dynamically complex working environments, and irregularly occluded targets. To address these limitations, we propose a robust tracking algorithm with occlusion resistance (hereinafter referred to as ROTrack). ROTrack compensates for the predictions of Kalman filter (KF) by incorporating Inertial Measurement Unit (IMU) information, enabling the tracker to achieve more accurate tracking in dynamic environments. Additionally, MobileSAM is employed to handle occlusion issues and obtain the correct three-dimensional coordinates of the targets. At the same time, a depth-triggered segmentation strategy is proposed to reduce computational resource consumption. The effect of ROTrack is demonstrated through alignment between IMU signals and Camera Motion Compensation (CMC) data in BoT-SORT. Real-world tracking tests validate the robustness and real-time capability of ROTrack.