Supervised manifold learning based on biased distance for view invariant body pose estimation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    In human body pose estimation, manifold learning is a useful method for reducing the dimension of 2D images and 3D body configuration data. Most commonly, body pose is estimated from silhouettes derived from images or image sequences. A major problem when applying manifold estimation, however, is its vulnerability to silhouette variation. In this paper, we propose a novel approach to solving viewpoint-induced silhouette variation by introducing biased label distances for learning manifolds that are able to represent variations in viewpoint, pose, and 3D body configuration. We demonstrate the effectiveness of the approach on a synthetic and a real-world dataset.

    Original languageEnglish
    Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
    Pages2717-2720
    Number of pages4
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
    Duration: 2012 Oct 142012 Oct 17

    Publication series

    NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
    ISSN (Print)1062-922X

    Other

    Other2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
    Country/TerritoryKorea, Republic of
    CitySeoul
    Period12/10/1412/10/17

    Keywords

    • Biased distance
    • Manifold learning
    • Pose estimation

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Control and Systems Engineering
    • Human-Computer Interaction

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