We propose a model-based tracking method, called appearance-guided particle filtering (AGPF), which integrates both sequential motion transition information and appearance information. A probability propagation model is derived from a Bayesian formulation for this framework, and a sequential Monte Carlo method is introduced for its realization. We apply the proposed method to articulated hand tracking, and show that it performs better than methods that only use either sequential motion transition information or only use appearance information.


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    Titel :

    Appearance-guided particle filtering for articulated hand tracking


    Beteiligte:
    Wen-Yan Chang, (Autor:in) / Chu-Song Chen, (Autor:in) / Yi-Ping Hung, (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    747209 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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