Probability hypothesis density filter for adjacent multi-target tracking

Mian Wu; Daikun Zheng; Junquan Yuan; Alei Chen; Chang Zhou; Wenfeng Chen

doi:10.1117/12.2588928

20 January 2021 Probability hypothesis density filter for adjacent multi-target tracking

Mian Wu, Daikun Zheng, Junquan Yuan, Alei Chen, Chang Zhou, Wenfeng Chen

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171918 (2021) https://doi.org/10.1117/12.2588928
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

In the adjacent multi-target scenario, the Gaussian mixture probability hypothesis density (GM-PHD) algorithm encounters problems of inaccurate target number estimation and low tracking accuracy. To tackle these problems, this paper proposes an improved components management strategy for GM-PHD algorithm. We develop a master-slave mode to process Gaussian components, the master components whose weights exceed the extraction threshold are retained to avoid merging them each other, which guarantees the accuracy of target number estimation. Meanwhile, the slave components which satisfying the merging conditions are merged with the corresponding master components to improve the target tracking accuracy. Simulation results show that the proposed algorithm can achieve better performance than conventional GM-PHD algorithm in different clutter environments.

Citation Download Citation

Mian Wu, Daikun Zheng, Junquan Yuan, Alei Chen, Chang Zhou, and Wenfeng Chen "Probability hypothesis density filter for adjacent multi-target tracking", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171918 (20 January 2021); https://doi.org/10.1117/12.2588928

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