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    Visual Tracking in Background Subtracted Image Sequences via Multi-Bernoulli Filtering

    Access Status
    Fulltext not available
    Authors
    Hoseinnezhad, R.
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Hoseinnezhad, Reza and Vo, Ba-Ngu and Vo, Ba-Tuong. 2013. Visual Tracking in Background Subtracted Image Sequences via Multi-Bernoulli Filtering. IEEE Transactions on Signal Processing. 61 (2): pp. 392-397.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2012.2222389
    ISSN
    1053-587X
    URI
    http://hdl.handle.net/20.500.11937/3820
    Collection
    • Curtin Research Publications
    Abstract

    This correspondence presents a novel method for simultaneous tracking of multiple non-stationary targets in video. Our method operates directly on the video data and does not require any detection. We propose a multi-target likelihood function for the background-subtracted grey-scale image data, which admits multi-target conjugate priors. This allows the multi-target posterior to be efficiently propagated forward using the multi-Bernoulli filter. Our method does not need any training pattern or target templates and makes no prior assumptions about object types or object appearance. Case studies from the CAVIAR dataset show that our method can automatically track multiple targets and quickly finds targets entering or leaving the scene.

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