:: Abstract & BibRef ::

Learning Dynamics for Exemplar-based Gesture Recognition

Abstract

This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed nonparametric HMM. [pdf] [Details]

@inproceedings{elgammal_cvpr03,
        AUTHOR    = "Elgammal, A. and Shet, V. and Yacoob, Y. and Davis, L.S.",
        TITLE     = "Learning Dynamics for Exemplar-based Gesture Recognition",
        BOOKTITLE = "IEEE International Conference on Computer Vision and 
		     Pattern Recognition (CVPR)",
        YEAR      = "2003",
        PAGES     = "I:571--578"
}