We present a novel technique for the recognition of complex human gestures for video annotation using accelerometers and the hidden Markov model. Our extension to the standard hidden Markov model allows us to consider gestures at different levels of abstraction through a hierarchy of hidden states. Accelerometers in the form of wrist bands are attached to humans performing intentional gestures, such as umpires in sports. Video annotation is then performed by populating the video with time stamps indicating significant events, where a particular gesture occurs. The novelty of the technique lies in the development of a probabilistic hierarchical framework for complex gesture recognition and the use of accelerometers to extract gestures and significant events for video annotation.
History
Event
International Conference on Pattern Recognition (16th : 2002 : Quebec, Canada)
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Publication classification
E1.1 Full written paper - refereed
Copyright notice
2002, IEEE
Editor/Contributor(s)
R Kasturi, D Laurendeau, C Suen
Title of proceedings
ICPR 2002 : Proceedings of the International Conference on Pattern Recognition