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    Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis

    20567_downloaded_stream_23.pdf (429.3Kb)
    Access Status
    Open access
    Authors
    Peursum, Patrick
    Venkatesh, Svetha
    West, Geoffrey
    Date
    2007
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Peursum, Patrick and Venkatesh, Svetha and West, Geoffrey. 2007. : Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis, IEEE Conference on Computer Vision and Patter Recognition 2007, 17 Jun 2007. Minneapolis, USA: IEEE Computer Society Conference Publishing Services.
    Source Title
    Computer Vision and Pattern Recognition 2007
    Source Conference
    IEEE Conference on Computer Vision and Patter Recognition 2007
    Faculty
    Faculty of Engineering and Computing
    Division of Engineering, Science and Computing
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/26678
    Collection
    • Curtin Research Publications
    Abstract

    This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model. The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

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