Dynamic gesture recognition with Laban Movement Analysis and Hidden Markov Models

Abstract : In this paper, we propose a new approach for gesture recognition based upon the quantification of Laban Movement Analysis (LMA) concepts. The resulting body features are used to build a dictionary of key poses. Then, a soft assignment method is applied to the gesture sequences to obtain a gesture representation. The assignment results are used as input in a Hidden Markov Models (HMM) scheme for dynamic gesture recognition purposes. The proposed approach achieves high recognition rates (more than 92% for certain categories of gestures), when tested and evaluated on a corpus including 11 different actions. The high recognition rates obtained on two other datasets (Microsoft Gesture dataset [1] and UTKinect-Human Detection dataset [2]) show the relevance of our method
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Communication dans un congrès
CGI 2016 : 33rd Computer Graphics International , Jun 2016, Heraklion, Greece. ACM, Proceedings CGI 2016 : 33rd Computer Graphics International pp.21 - 24, 2016, 〈10.1145/2949035.2949041〉
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https://hal.archives-ouvertes.fr/hal-01451527
Contributeur : Médiathèque Télécom Sudparis & Télécom Ecole de Management <>
Soumis le : mercredi 1 février 2017 - 11:22:14
Dernière modification le : mardi 10 octobre 2017 - 13:43:58

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Arthur Truong, Titus Zaharia. Dynamic gesture recognition with Laban Movement Analysis and Hidden Markov Models. CGI 2016 : 33rd Computer Graphics International , Jun 2016, Heraklion, Greece. ACM, Proceedings CGI 2016 : 33rd Computer Graphics International pp.21 - 24, 2016, 〈10.1145/2949035.2949041〉. 〈hal-01451527〉

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