Multi-Object Tracking Algorithm of Fusing Trajectory Compensation

Jianhai Jin, Liming Wang, Qi You and Jun Sun
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Jianhai Jin: China Ship Scientific Research Center, No. 265 Shanshui East Road, Wuxi 214082, China
Liming Wang: School of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenue, Wuxi 214122, China
Qi You: School of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenue, Wuxi 214122, China
Jun Sun: School of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenue, Wuxi 214122, China

Mathematics, 2022, vol. 10, issue 15, 1-17

Abstract: Multi-object tracking (MOT) is an important research topic in the field of computer vision, including object detection and data association. However, problems such as missed detection and trajectory mismatch often lead to missing target information, thus resulting in missed target tracking and trajectory fragmentation. Uniform tracking confidence is also not conducive to the full utilization of detection results. Considering these problems, we first propose a threshold separation strategy, which sets different tracking thresholds for similarity matching and intersection over union (IoU) matching during association to make the distribution of detection information more reasonable. Then, the missing trajectories are screened and compensated with the predicted trajectories to improve the long-term tracking ability of the algorithm. When applied to different association algorithms or tracking algorithms, a better improvement effect can be obtained. It can achieve high tracking speed while achieving high tracking accuracy on the MOT Challenge dataset.

Keywords: multi-object tracking; data association; trajectory compensation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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