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    Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model

    Access Status
    Fulltext not available
    Authors
    Duong, Thi
    Phung, Dinh
    Bui, H.H.
    Venkatesh, Svetha
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Duong, T. and Phung, D. and Bui, H.H. and Venkatesh, S. 2006. Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model, in Tang, Y.Y. et al(ed), Proceedings of the 18th International Conference on Pattern Recognition, Aug 20-24 2006, pp. 202-207. Hong Kong: IEEE.
    Source Title
    Proceedings of the 18th International Conference on Pattern Recognition Vol 3
    Source Conference
    International Conference on Pattern Recognition 2006
    DOI
    10.1109/ICPR.2006.635
    ISBN
    0769525210
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/46984
    Collection
    • Curtin Research Publications
    Abstract

    The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.

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