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    A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

    Access Status
    Fulltext not available
    Authors
    Tran, Dung
    Phung, Dinh
    Bui, H.H.
    Venkatesh, Svetha
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Tran, D. and Phung, D. and Bui, H.H. and Venkatesh, S. 2006. A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment, in Y.Y. Tang et al (ed), Proceedings of the 18th International Conference on Pattern Recognition, Aug 20-24 2006, pp. 168-172. 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.2006.154
    ISBN
    0-7695-2521-0
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/10676
    Collection
    • Curtin Research Publications
    Abstract

    To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.

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