Laban movement analysis for real-time 3D gesture recognition

Abstract : In this paper, we propose a new method for body gesture recognition based upon Laban Movement Analysis (LMA). The features are computed for a dataset of pre-segmented sequences putting at stake 11 different actions, and are used to build a dictionary of key poses, obtained with the help of a k-means clustering approach. A soft assignment method based upon the obtained poses is applied to the dataset and assignment results are used as input sequences in a Hidden Markov Models (HMM) framework for real-time action recognition purpose. The high recognition rates obtained (more than 92% for certain gestures), demonstrate the pertinence of the proposed method
Type de document :
Communication dans un congrès
MEASURING BEHAVIOR 2016 : 10th International Conference on Methods and Techniques in Behavioral Research, May 2016, Dublin, Ireland. A.J. Spink et al. (Eds.), Proceedings MEASURING BEHAVIOR 2016 : 10th International Conference on Methods and Techniques in Behavioral Research, pp.514 - 521, 2016
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https://hal.archives-ouvertes.fr/hal-01451739
Contributeur : Médiathèque Télécom Sudparis & Télécom Ecole de Management <>
Soumis le : mercredi 1 février 2017 - 14:09:19
Dernière modification le : jeudi 9 février 2017 - 16:13:12

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  • HAL Id : hal-01451739, version 1

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Arthur Truong, Titus Zaharia. Laban movement analysis for real-time 3D gesture recognition. MEASURING BEHAVIOR 2016 : 10th International Conference on Methods and Techniques in Behavioral Research, May 2016, Dublin, Ireland. A.J. Spink et al. (Eds.), Proceedings MEASURING BEHAVIOR 2016 : 10th International Conference on Methods and Techniques in Behavioral Research, pp.514 - 521, 2016. <hal-01451739>

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