Hybrid multiple-object tracker incorporating Particle Swarm Optimization and Particle Filter

Chen-Chien James Hsu, Yung Ching Chu, Ming Chih Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study presents a hybrid algorithm incorporating Particle Swarm Optimization (PSO) and Particle Filter (PF) for multiple-object tracking based mainly on gray-level histogram model. To start with, the hybrid object tracker uses PSO to search the objects in the beginning, taking advantage of the PSO for global optimization. Once the objects have been successfully found by PSO, the hybrid object tracker then switches to PF to continuously track the objects. To avoid the varying-size problem of the objects, Speeded Up Robust Features (SURF) is used to detect the object around its neighborhood in the video sequence for defining the real image size of the object for remodeling the target object by histogram. As a result, tracking speed can be maintained by the hybrid tracker using simple histogram model while circumventing the varying-size problem of the objects during the tracking process.

Original languageEnglish
Title of host publicationICSSE 2013 - IEEE International Conference on System Science and Engineering, Proceedings
Pages189-193
Number of pages5
DOIs
Publication statusPublished - 2013 Nov 18
EventIEEE International Conference on System Science and Engineering, ICSSE 2013 - Budapest, Hungary
Duration: 2013 Jul 42013 Jul 6

Publication series

NameICSSE 2013 - IEEE International Conference on System Science and Engineering, Proceedings

Other

OtherIEEE International Conference on System Science and Engineering, ICSSE 2013
CountryHungary
CityBudapest
Period13/7/413/7/6

ASJC Scopus subject areas

  • Control and Systems Engineering

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