View invariant body pose estimation based on biased manifold learning

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

    6 Citations (Scopus)

    Abstract

    In human body pose estimation, manifold learning is a popular technique for reducing the dimension of 2D images and 3D body configuration data. This technique, however, is especially vulnerable to silhouette variation such as caused by viewpoint changes. In this paper, we propose a novel approach that combines three separate manifolds for representing variations in viewpoint, pose and 3D body configuration. We use biased manifold learning to learn these manifolds with appropriately weighted distances. A set of four mapping functions are then learned by a generalized regression neural network for added robustness. Despite using only three manifolds, we show that this method can reliably estimate 3D body poses from 2D images with all learned viewpoints.

    Original languageEnglish
    Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
    Pages3866-3869
    Number of pages4
    DOIs
    Publication statusPublished - 2010
    Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
    Duration: 2010 Aug 232010 Aug 26

    Publication series

    NameProceedings - International Conference on Pattern Recognition
    ISSN (Print)1051-4651

    Other

    Other2010 20th International Conference on Pattern Recognition, ICPR 2010
    Country/TerritoryTurkey
    CityIstanbul
    Period10/8/2310/8/26

    Keywords

    • Body pose analysis
    • Manifold learning
    • Non-linear dimensional reduction
    • Supervised learning
    • View-invariance

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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