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.
Highlights
Gestures are generally defined as motions of the body that contain meaningful information [1]
The emergence of general public, affordable depth cameras (e.g., Kinect) facilitating 3D body tracking can explain the recent growth of interest for gesture analysis [2,3,4,5,6,7]
Gesture analysis and interpretation is highly useful for numerous applications: e-health, video games, artistic creation, video surveillance, immersive and affective communication
Summary
Gestures are generally defined as motions of the body that contain meaningful information [1]. Within this framework, action analysis is a highly challenging issue that involves both computer vision and machine learning methodologies. The generic objective is to semantically qualify body movements, postures, gestures, or actions with the help of mid or high-level features built upon low-level visual features. Gesture analysis and interpretation is highly useful for numerous applications: e-health, video games, artistic creation, video surveillance, immersive and affective communication. The issue of high level, semantic interpretation of gestures still remains a challenge, which requires the elaboration and development of effective gesture descriptors and recognition algorithms. A few papers propose effective models of gesture descriptors [8,9,10,11] and such models rarely
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