An Anti-occlusion Adaptive Particle Filtering Algorithm

doi:10.16182/j.issn1004731x.joss.201809041

Abstract

Abstract: Considering the influence of local occlusion on particle filter tracking algorithm, an anti occlusion adaptive particle filtering algorithm is proposed. It adopts a rectangle as the tracking window, and uses the K mean clustering algorithm to complete particle clustering in resampling, and then obtains the particle subgroup. It estimates the final state according to particles subgroups, and modifies the tracking window. When the area changes more than 5%, the tracking window maintains the same as the one in last frame. Otherwise, the tracking window will change according to the size of moving object, which is a self-adaptation process. At the same time it solves the degeneration problem of particle filter. This algorithm strengthens the robustness of tracking algorithm in case of local occlusion and moving object scale changing. The method performs better than the traditional particle filter tracking algorithm.

Key words: anti-occlusion, KM clustering, particle filter, moving object tracking

CLC Number:

TP391.41

Li Ju, Cao Mingwei, Yu Ye, XiaYu, Zhou Lifan. An Anti-occlusion Adaptive Particle Filtering Algorithm[J]. Journal of System Simulation, 2018, 30(9): 3552-3557.

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