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A method for learning, tracking, and recognizing human gestures using a view-based approach to model articulated objects is presented. Objects are represented using sets of view models, rather than single templates. Stereotypical space-time patterns, i.e., gestures, are then matched to stored gesture patterns using dynamic time warping. Real-time performance is achieved by using special purpose correlation hardware and view prediction to prune as much of the search space as possible. Both view models and view predictions are learned from examples. Results showing tracking and recognition of human hand gestures at over 10 Hz are presented.>
Darrell et al. (Mon,) studied this question.