A Fuzzy Framework for Real-Time Gesture Spotting and Recognition

Abstract

A vital requirement of any recognition system claiming to be real time is the capability to perform feature extraction in real time. In this paper, we propose an innovative fuzzy approach for real-time dynamic gesture recognition and spotting, where a compact local descriptor is designed to model moving gesture skeletons as a time series of fuzzy statistical features. Then, a set of one-vs-rest SVMs is trained on these features for gesture recognition and spotting. In this approach, the meaningful hand movements are successfully spotted while concurrently removing unintentional hand movements from an input video sequence. When evaluated on a gesture data set incorporating a relatively large and diverse collection of video data, the method proposed yields promising results that compare very favorably with those reported in the literature, while retaining real-time performance.