This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points' trajectories. Peaks and valleys of these trajectories are used to detect key frames-frames where both legs are in contact with the floor. Secondly, motion models associated with each joint are locally tuned by using those key frames. Differently than previous approaches, this tuning process is not performed at every frame, reducing CPU time. In addition, the movement's frequency is defined by the elapsed time between two consecutive key frames, which allows handling walking displacement at different speed. Experimental results with different video sequences are presented.


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    Title :

    Prior Knowledge Based Motion Model Representation


    Contributors:

    Publication date :

    2005-11-01


    Remarks:

    doi:10.5565/rev/elcvia.106
    ELCVIA Electronic Letters on Computer Vision and Image Analysis; Vol. 5 No. 3 (2005): Special issue on Articulated Motion & Deformable Objects - August 2005; 55 - 67 ; 1577-5097



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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