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    AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition

    Access Status
    Fulltext not available
    Authors
    Truyen, Tran
    Phung, Dinh
    Bui, H.
    Venkatesh, Svetha
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Truyen, T. and Phung, D. and Bui, H. and Venkatesh, S. 2006. AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition, in Fitzgibbon, A. and Taylor, C. and LeCun, Y. (ed), IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun 17-26 2006, pp. 1686-1693. New York, USA: IEEE Computer Society Conference Publishing Services.
    Source Title
    2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Source Conference
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006
    DOI
    10.1109/CVPR.2006.49
    ISBN
    0769525970
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/16060
    Collection
    • Curtin Research Publications
    Abstract

    Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov random field (MRF), known as the dynamic conditional random field (DCRF). Parameter estimation in general MRFs using maximum likelihood is known to be computationally challenging (except for extreme cases), and thus we propose an efficient boosting-based algorithm AdaBoost.MRF for this task. Distinct from most existing work, our algorithm can handle hidden variables (missing labels) and is particularly attractive for smarthouse domains where reliable labels are often sparsely observed. Furthermore, our method works exclusively on trees and thus is guaranteed to converge. We apply the AdaBoost.MRF algorithmto a home video surveillance application and demonstrate its efficacy.

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