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 publicationImage Analysis and Recognition - Second International Conference, ICIAR 2005, Proceedings
    PublisherSpringer Verlag
    Pages852-860
    Number of pages9
    ISBN (Print)3540290699, 9783540290698
    DOIs
    Publication statusPublished - 2005
    Event2nd International Conference on Image Analysis and Recognition, ICIAR 2005 - Toronto, Canada
    Duration: 2005 Sept 282005 Sept 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
    Country/TerritoryCanada
    CityToronto
    Period05/9/2805/9/30

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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