Multi-cue Exemplar-based Nonparametric Model for Gesture Recognition

Abstract

This paper presents an approach for a multi-cue, view based recognition of gestures. We describe an exemplar based technique that combines two different forms of exemplars - shape exemplars and motion exemplars - in a unified probabilistic framework. Each gesture is represented as a sequence of learned body poses as well as a sequence of learned motion parameters. The shape exemplars are comprised of pose contours, and the motion exemplars are represented as affine motion parameters extracted using a robust estimation approach. The probabilistic framework learns by employing a nonparametric estimation technique to model the exemplar distributions. It imposes temporal constraints between different exemplars through a learned Hidden Markov Model (HMM) for each gesture. We use the proposed multi-cue approach to recognize a set of fourteen gestures and contrast it against a shape only, single cue based system.[pdf] [Details]

BibRef

@inproceedings{shet_icvgip04,
        AUTHOR    = "Shet, V.D. and Nagaprasad, V.S. and Elgammal, A. and Yacoob, Y. 
		     and Davis, L.S.",
        TITLE     = "Multi-cue Exemplar-based Nonparametric Model for 
		     Gesture Recognition",
        BOOKTITLE = "Indian Conference on Computer Vision, Graphics and 
		     Image Processing(ICVGIP)", 
        YEAR      = "2004",
        PAGES     = "656--662"
}