A modified PSO based particle filter algorithm for object tracking

Yufei Tang; Siyao Fu; Bo Tang; Haibo He

doi:10.1117/12.2017977

29 May 2013 A modified PSO based particle filter algorithm for object tracking

Yufei Tang, Siyao Fu, Bo Tang, Haibo He

Proceedings Volume 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI; 87500S (2013) https://doi.org/10.1117/12.2017977
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with ε- greedy exploration εPSO), is used to tackle the object tracking. In the modified εPSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The εPSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new εPSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our εPSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.

Citation Download Citation

Yufei Tang, Siyao Fu, Bo Tang, and Haibo He "A modified PSO based particle filter algorithm for object tracking", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500S (29 May 2013); https://doi.org/10.1117/12.2017977

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