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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    Title :

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


    Contributors:
    Davis, (author) / Hui Gao, (author)


    Publication date :

    2003-01-01


    Size :

    3742437 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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