We present a computational framework capable of labeling the effort of an action corresponding to the perceived level of exertion by the performer (low - high). The approach initially factorizes examples (at different efforts) of an action into its three-mode principal components to reduce the dimensionality. Then a learning phase is introduced to compute expressive-feature weights to adjust the model's estimation of effort to conform to given perceptual labels for the examples. Experiments are demonstrated recognizing the efforts of a person carrying bags of different weight and for multiple people walking at different paces.


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    Titel :

    Recognizing human action efforts: an adaptive three-mode PCA framework


    Beteiligte:
    Davis, (Autor:in) / Hui Gao, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    3742437 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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