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Dynamic gesture recognition for natural human system interaction

Abstract : This paper addresses two problems: 3d dynamic gesture recognition and gesture misallocation. In order to solve these problems, we propose a new approach which combines Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). The proposed approach has two main phases; first, recognizing gestures using a hidden Markov model. Second, avoiding misallocation by rejecting gestures based on a threshold computed using DTW. Our database includes many samples of five gestures obtained with a Kinect and described by depth information only. The results show that our approach yields good gesture classification without any misallocation and it is robust against environmental constraints.
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Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Thursday, October 13, 2016 - 10:16:33 AM
Last modification on : Saturday, May 1, 2021 - 3:49:18 AM


  • HAL Id : hal-01380425, version 1


Hajar Hiyadi, Fakhr-Eddine Ababsa, Christophe Montagne, El Houssine Bouyakhf, Fakhita Regragui. Dynamic gesture recognition for natural human system interaction. Journal of Theoretical and Applied Information Technology, JATIT, 2016, 91 (2), pp.374--383. ⟨hal-01380425⟩



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