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    Joint detection and estimation of multiple objects from image observation

    Access Status
    Fulltext not available
    Authors
    Vo, Ba-Ngu
    Vo, Ba Tuong
    Pham, N.
    Suter, D.
    Date
    2010
    Type
    Journal Article
    
    Metadata
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    Citation
    Vo, B. and Vo, B.T. and Pham, N. and Suter, D. 2010. Joint detection and estimation of multiple objects from image observation. IEEE Transactions on Signal Processing. 58 (10): pp. 5129-5141.
    Source Title
    IEEE Transactions on Signal Processing
    DOI
    10.1109/TSP.2010.2050482
    ISSN
    1053-587X
    URI
    http://hdl.handle.net/20.500.11937/15926
    Collection
    • Curtin Research Publications
    Abstract

    The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations.

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