A Framework for Motion Recognition with Applications to American Sign Language and Gait Recognition

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Vogler, Christian
Sun, Harold
Metaxas, Dimitris

Human motion recognition has many important applications, such as improved human-computer interaction and surveillance. A big problem that plagues this research area is that human movements can be very complex. Managing this complexity is difficult. We turn to American Sign Language (ASL) recognition to identify general methods that reduce the complexity of human motion recognition. In this paper we present a framework for continuous 30 ASL recognition based on linguistic principles, especially the phonology of ASL. This framework is based on parallel Hidden Markov Models (HMMs), which are able to capture both the sequential and the simultaneous aspects of the language. Each HMM is based on a single phoneme of ASL. Because the phonemes are limited in number, as opposed to the virtually unlimited number of signs that can be composed from them, we expect this framework to scale well to larger applications. We then demonstrate the general applicability of this framework to other human motion recognition tasks by extending it to gait recognition.

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Copyright 2000 IEEE. Reprinted from <em>Proceedings of the Workshop on Human Motion</em>, December 2000, pages 33-38. Publisher URL: http://dx.doi.org/10.1109/HUMO.2000.897368 <br><br> This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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