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

Arthur Truong (1) , Titus Zaharia (2, 1, 3, 4, 5)

1 TSP - ARTEMIS - Département Advanced Research And Techniques For Multidimensional Imaging Systems
2 ARMEDIA-SAMOVAR - ARMEDIA
3 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
4 MAP5 - UMR 8145 - Mathématiques Appliquées Paris 5
5 CNRS - Centre National de la Recherche Scientifique

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

Mots clés: Laban movement analysis; Gesture recognition; Body motion descriptors; Soft assignment; Hidden Markov model

Domaines: Traitement du signal et de l'image [eess.SP]

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⟩