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    Visual multiple-object tracking for unknown clutter rate

    Access Status
    Fulltext not available
    Authors
    Kim, Du Yong
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Kim, D.Y. 2018. Visual multiple-object tracking for unknown clutter rate. IET Computer Vision. 12 (5): pp. 728-734.
    Source Title
    IET Computer Vision
    DOI
    10.1049/iet-cvi.2017.0600
    ISSN
    1751-9632
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/73041
    Collection
    • Curtin Research Publications
    Abstract

    © The Institution of Engineering and Technology 2018. In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this study, the authors are interested in designing a multi-object tracking algorithm that handles unknown false measurement rate. The recently proposed robust multi-Bernoulli filter is employed for clutter estimation while generalised labelled multi-Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real-world scenarios.

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