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Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

Adaptive Gesture Recognition with Variation Estimation for Interactive Systems Adaptive Gesture Recognition with Variation Estimation for Interactive Systems BAPTISTE CARAMIAUX, Goldsmiths, University of London, UK/IRCAM, Paris, France NICOLA MONTECCHIO, University of Padova, Italy ATAU TANAKA, Goldsmiths, University of London ´ ´ FREDERIC BEVILACQUA, STMS Lab IRCAM-CNRS-UPMC, Paris, France This article presents a gesture recognition/adaptation system for human­computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human­machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Adaptive Gesture Recognition with Variation Estimation for Interactive Systems

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
2160-6455
DOI
10.1145/2643204
Publisher site
See Article on Publisher Site

Abstract

Adaptive Gesture Recognition with Variation Estimation for Interactive Systems BAPTISTE CARAMIAUX, Goldsmiths, University of London, UK/IRCAM, Paris, France NICOLA MONTECCHIO, University of Padova, Italy ATAU TANAKA, Goldsmiths, University of London ´ ´ FREDERIC BEVILACQUA, STMS Lab IRCAM-CNRS-UPMC, Paris, France This article presents a gesture recognition/adaptation system for human­computer interaction applications that goes beyond activity classification and that, as a complement to gesture labeling, characterizes the movement execution. We describe a template-based recognition method that simultaneously aligns the input gesture to the templates using a Sequential Monte Carlo inference technique. Contrary to standard template-based methods based on dynamic programming, such as Dynamic Time Warping, the algorithm has an adaptation process that tracks gesture variation in real time. The method continuously updates, during execution of the gesture, the estimated parameters and recognition results, which offers key advantages for continuous human­machine interaction. The technique is evaluated in several different ways: Recognition and early recognition are evaluated on 2D onscreen pen gestures; adaptation is assessed on synthetic data; and both early recognition and adaptation are evaluated in a user study involving 3D free-space gestures. The method is robust to noise, and successfully adapts to parameter variation. Moreover, it performs recognition as well

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Dec 19, 2014

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