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    Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association

    Access Status
    Fulltext not available
    Authors
    Nguyen, Nam
    Venkatesh, Svetha
    Bui, H.H.
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nguyen, N. and Venkatesh, S. and Bui, H.H. 2006. Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association, in Chantler, M. and Fisher, R. and Trucco, E. (ed), 17th British Machine Vision Conference (BMVC), Sep 4-7 2006. Edinburgh, Scottland: The British Machine Vision Association and Society for Pattern Recognition.
    Source Title
    17th British Machine Vision Conference Proceedings
    Source Conference
    17th British Machine Vision Conference 2006
    Additional URLs
    http://www.macs.hw.ac.uk/bmvc2006/papers/190.pdf
    ISBN
    1-904410-14-6
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/15276
    Collection
    • Curtin Research Publications
    Abstract

    Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint probabilistic data association filters (JPD AF) is applied for data association. The main contributions of this paper lie in the integration of multiple HHMMs for recognising high-le v el behaviours of multiple people and the construction of the Rao-Blackwellised particle filters (RBPF) for approximate inference. Preliminary experimental results in a real environment show the robustness of our integrated method in behaviour recognition and its advantage over the use of Kalman filter in tracking people.

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