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    Multi-modal abnormality detection in video with unknown data segmentation

    Access Status
    Fulltext not available
    Authors
    Nguyen, T.
    Rana, S.
    Phung, D.
    Pham, DucSon
    Venkatesh, S.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Nguyen, Tien Vu and Phung, Dinh and Rana, Santu and Pham, Duc Son and Venkatesh, Svetha. 2012. Multi-modal abnormality detection in video with unknown data segmentation, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), Nov 11-15 2012, pp. 1322-1325. Tsukuba, Japan: IEEE.
    Source Title
    Proceedings of the International Conference on Pattern Recognition
    Source Conference
    The International Conference on Pattern Recognition
    Additional URLs
    http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460383
    ISBN
    9784990644116
    URI
    http://hdl.handle.net/20.500.11937/42528
    Collection
    • Curtin Research Publications
    Abstract

    This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently pro-posed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.

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