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    Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

    116822_Learning%20and%20detecting%20activities%20PID%20116822.pdf (219.1Kb)
    Access Status
    Open access
    Authors
    Nguyen, Nam
    Phung, Dinh
    Venkatesh, Svetha
    Bui, Hung H.
    Date
    2005
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nguyen, Nam and Phung, Dinh and Venkatesh, Svetha and Bui, Hung. 2005. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model, in Schnid, C. and Soatto, S. and Tomasi, C. (ed), Conference on Computer Vision and Pattern Recognition (CVPR 2005), Jun 20 2005, Vol. 2: pp. 955-960. San Diego, USA: IEEE Computer Society Press.
    Source Title
    Proceedings 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Source Conference
    Conference on Computer Vision and Pattern Recognition (CVPR 2005)
    DOI
    10.1109/CVPR.2005.203
    ISBN
    0769523722
    ISSN
    10636919
    Faculty
    School of Electrical Engineering and Computing
    Department of Computing
    Faculty of Science and Engineering
    Remarks

    Copyright © 2005 IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/15305
    Collection
    • Curtin Research Publications
    Abstract

    Directly modelling the inherent hierarchy and shared structures of human behaviours, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model?s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modelling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM.

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