Predictive estimation method to track occluded multiple objects using joint probabilistic data association filter

Heungkyu Lee, Hanseok Ko

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

3 Citations (Scopus)

Abstract

In multi-target visual tracking, tracking failure due to miss-association can often arise from the presence of occlusions between targets. To cope with this problem, we propose the predictive estimation method that iterates occlusion prediction and occlusion status update using occlusion activity detection by utilizing joint probabilistic data association filter in order to track each target before, during and after occlusion. First, the tracking system predicts the position of a target, and occlusion activity detection is performed at the predicted position to examine if an occlusion activity is enabled. Second, the tracking system re-computes positions of occluded targets and updates them if an occlusion activity is enabled. Robustness of multi-target tracking using predictive estimation method is demonstrated with representative simulations.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages852-860
Number of pages9
Publication statusPublished - 2005 Dec 1
Event2nd International Conference on Image Analysis and Recognition, ICIAR 2005 - Toronto, Canada
Duration: 2005 Sep 282005 Sep 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3656 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Conference on Image Analysis and Recognition, ICIAR 2005
CountryCanada
CityToronto
Period05/9/2805/9/30

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, H., & Ko, H. (2005). Predictive estimation method to track occluded multiple objects using joint probabilistic data association filter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 852-860). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3656 LNCS).