Hand gestures are examples of fast and complex motions. Computers fail to track these in fast video, but sleight of hand fools humans as well: what happens too quickly we just cannot see. We show a 3D tracker for these types of motions that relies on the recognition of familiar configurations in 2D images (classification), and fills the gaps in-between (interpolation). We illustrate this idea with experiments on hand motions similar to finger spelling. The penalty for a recognition failure is often small: if two configurations are confused, they are often similar to each other, and the illusion works well enough, for instance, to drive a graphics animation of the moving hand. We contribute advances in both feature design and classifier training: our image features are invariant to image scale, translation, and rotation, and we propose a classification method that combines VQPCA with discrimination trees.


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    Titel :

    3D tracking = classification + interpolation


    Beteiligte:
    Tomasi, (Autor:in) / Petrov, (Autor:in) / Sastry, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    393880 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    3D Tracking = Classification + Interpolation

    Tomasi, C. / Petrov, S. / Sastry, A. et al. | British Library Conference Proceedings | 2003



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