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

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.