Detection and feature fusion of trajectory Poisson multi-Bernoulli mixture algorithms for visual multiobject tracking

Guochen Zhang; Qinmu Shen; Zhejun Lu

doi:10.1117/12.3050892

24 October 2024 Detection and feature fusion of trajectory Poisson multi-Bernoulli mixture algorithms for visual multiobject tracking

Guochen Zhang, Qinmu Shen, Zhejun Lu

Author Affiliations +

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960Q (2024) https://doi.org/10.1117/12.3050892
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

In a video multi-object tracking scene, the problems of target occlusion, track crossing still exist. Limited by the quantity of the detector, the traditional association algorithms will lead to missing and false tracking and increase the number of target ID switch and trajectory fragmentation. Aiming at these problems, this paper proposes an optimized algorithm based on the framework of Trajectory Poisson Multi-Bernoulli Mixture Filter, combines the joint detection and embedding algorithm to output both targets’ detections and features, designs new association and update algorithm to improve the trajectory maintenance. The result shows that the proposed algorithm can effectively decrease the number of ID switch and maintain the trajectories when tracking with short-time occlusions and crossing. It is tested on MOT datasets and gets a positive effect compared with other algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guochen Zhang, Qinmu Shen, and Zhejun Lu "Detection and feature fusion of trajectory Poisson multi-Bernoulli mixture algorithms for visual multiobject tracking", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960Q (24 October 2024); https://doi.org/10.1117/12.3050892

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