We propose a statistical formulation for 2D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to infer pose parameters from image cues such as appearance, shape, edge, and color. From a set of hand labeled images, we accumulate prior knowledge of 2D body shapes by learning their low-dimensional representations for inference of pose parameters. A data driven belief propagation Monte Carlo algorithm, utilizing importance sampling functions built from bottom-up visual cues, is proposed for efficient probabilistic inference. Contrasted to the few sequential statistical formulations in the literature, our algorithm integrates both top-down as well as bottom-up reasoning mechanisms, and can carry out the inference tasks in parallel. Experimental results demonstrate the potency and effectiveness of the proposed algorithm in estimating 2D human pose from single images.


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    Titel :

    Learning to estimate human pose with data driven belief propagation


    Beteiligte:
    Gang Hua, (Autor:in) / Ming-Hsuan Yang, (Autor:in) / Ying Wu, (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    586506 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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