We present a robust framework for real-time processing of 3D motion capture data. It autonomously analyzes the input data to build a model of the observed subjects, and is expected to perform in situations where the observed features are unstable or frequently occluded. We have implemented the method and presently rely on a marker based motion capture system to provide unlabeled 3D coordinates of non-occluded markers. However, we hope this to be a step towards using regular video camera systems with feature detection to perform autonomous markerless motion capture.


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    Titel :

    Autonomous real-time model building for optical motion capture


    Beteiligte:
    Rajko, S. (Autor:in) / Qian, G. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    112672 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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