Enhancing Particle Swarm Optimization Based Particle Filter Tracker

A novel particle filter, enhancing particle swarm optimization based particle filter (EPSOPF), is proposed for visual tracking. Particle filter (PF) is sequential Monte Carlo simulation based on particle set representations of probability densities, which can be applied to visual tracking. However, PF has the impoverishment phenomenon which limits its application. To improve the performance of PF, particle swarm optimization with mutation operator is introduced to form new filtering, in which mutation operator maintain multiple modes of particle set and optimization-seeking procedure drives particles to their neighboring maximum of the posterior. When applied to visual tracking, the proposed approach can realize more efficient function than PF.

Authors and Affiliations

1. Departmant of Information and Electronics Engineering, Zhejiang University, Hangzhou, Zhejiang 310013, China

   Qicong Wang, Li Xie, Jilin Liu & Zhiyu Xiang

Editors and Affiliations

1. Institute of Intelligent Machines , Chinese Academy of Sciences , Anhui, Hefei, China

   De-Shuang Huang

2. Queen’s University , Belfast, UK

   Kang Li  & George William Irwin  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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