Continuous Stereo Gesture Recognition with Multi-layered Silhouette Templates and Support Vector Machines

Abstract

This paper presents a novel approach for continuous gesture recognition using depth range sensors. Our approach can be seen as an extension of Motion Templates [1] using multiple layers that register the three-dimensional nature of the human gestures. Our Multi-Layered templates are created using depth silhouettes, the extension of binary silhouettes when depth information is available. Both the original Motion Templates and our extension have been tested using several classification approaches in order to determine the best one. These approaches include the use of Hu-moments (originally employed in [1]), PCA and Support Vector Machines. Finally, we propose a methodology for creating a continuous gesture recogniser using motion templates. The methodology is applied both to our representation approach and to the original proposal. In order to validate our proposal, several stereo-video sequences have been recorded showing eight people performing a total of ten different gestures that are prone to be confused when monocular vision is used. The conducted experiments show that our proposal performs a 20% better than the original method.