Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition

Abstract : In this paper, we propose a new approach for body gesture recognition. The body motion features considered quantify a set of Laban Movement Analysis (LMA) concepts. These features are used to build a dictionary of reference poses, obtained with the help of a k-medians clustering technique. 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, real-time 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 and UTKinect-Human Detection dataset) show the relevance of our method
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EURASIP Journal on image and video processing, 2017, 2017 (1), pp.52-1 - 52-16. 〈10.1186/s13640-017-0202-5〉
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https://hal.archives-ouvertes.fr/hal-01687265
Contributeur : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Soumis le : jeudi 18 janvier 2018 - 12:01:15
Dernière modification le : jeudi 31 mai 2018 - 09:12:02

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Arthur Truong, Titus Zaharia. Laban movement analysis and hidden Markov models for dynamic 3D gesture recognition. EURASIP Journal on image and video processing, 2017, 2017 (1), pp.52-1 - 52-16. 〈10.1186/s13640-017-0202-5〉. 〈hal-01687265〉

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