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Enhancing Particle Swarm Optimization Based Particle Filter Tracker

  • Qicong Wang
  • Li Xie
  • Jilin Liu
  • Zhiyu Xiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4114)

Abstract

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.

Keywords

Particle Swarm Optimization Particle Filter Posterior Density Visual Tracking Tracking Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qicong Wang
    • 1
  • Li Xie
    • 1
  • Jilin Liu
    • 1
  • Zhiyu Xiang
    • 1
  1. 1.Departmant of Information and Electronics Engineering, Zhejiang University, Hangzhou, Zhejiang 310013China

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