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Multi-target tracking using separated importance sampling particle filters with joint image likelihood

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In visual tracking, Multi-target tracking (MTT) systems encounter the problem that unavoidably moving targets may overlap each other and the measurement process of each target becomes dependent, so we construct a tracking system with considering joint image likelihood to track recognize targets, even homogeneous ones. Also, in order to enhance the tracking performance, we extend the sequential importance sampling (SIS) particle filter with the separated importance functions for tracking each target and detection at the same time. The overall performance is validated in the experiments and shows the robustness with near real-time tracking.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5179-5184
Number of pages6
ISBN (Print)1424401003, 9781424401000
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan
Duration: 8 Oct 200611 Oct 2006

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume6
ISSN (Print)1062-922X

Conference

Conference2006 IEEE International Conference on Systems, Man and Cybernetics
Country/TerritoryTaiwan
CityTaipei
Period8/10/0611/10/06

Keywords

  • Joint likelihood
  • Multi-target tracking
  • Particle filters
  • Sequential Monte Carlo
  • Visual tracking

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