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    Multiple object tracking in unknown backgrounds with labeled random finite sets

    Access Status
    Fulltext not available
    Authors
    Punchihewa, Y.
    Vo, Ba Tuong
    Vo, B.
    Kim, Du Yong
    Date
    2018
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Punchihewa, Y. and Vo, B.T. and Vo, B. and Kim, D.Y. 2018. Multiple object tracking in unknown backgrounds with labeled random finite sets. IEEE Transactions on Signal Processing. 66 (11): pp. 3040-3055.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2018.2821650
    ISSN
    1053-587X
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/66760
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes an online multiple object tracker that can operate under unknown detection profile and clutter rate. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time; hence, the ability of the algorithm to adaptively learn these parameters is essential in practice. In this paper, we detail how the generalized labeled multibernoulli filter, a tractable and provably Bayes optimal multiobject tracker, can be tailored to learn clutter and detection parameters on-the-fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance.

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