A Non-temporal Approach for Gesture Recognition Using Microsoft Kinect
Gesture recognition has become a very active research area with the advent of the Kinect sensor. The most common approaches for gesture recognition use temporal information and are based on methods such as Hidden Markov Models (HMM) and Dynamic Time Warping (DTW). In this paper, we present a novel non-temporal alternative for gesture recognition using the Microsoft Kinect device. The proposed approach, Recognition by Characteristic Window (RCW), identifies, using clustering techniques and a sliding window, distinctive portions of individual gestures which have low overlapping information with other gestures. Once a distinctive portion has been identified for each gesture, all these sub-sequences are used to recognize a new instance. The proposed method was compared against HMM and DTW on a benchmark gesture’s dataset showing very competitive performance.
KeywordsMachine Learning Gesture Recognition Kinect
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