Robust hand gesture detection by fusion of depth and colour information using kinect
Shuai Yang, Prashan Premaratne, Peter Vial, Qasim Alshebani
COMPUTER MODELLING & NEW TECHNOLOGIES 2014 18(12B) 127-132
School of Electrical Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia
Microsoft Kinect camera has drastically changed the world of human computer interaction based computer vision, due to its low cost and high quality of depth information for visual images. This has made the depth data to become common place at a very low cost allowing myriad of computer vision related application including hand gesture recognition. Hand gesture recognition research suffered severely from the clutter and skin tone regions in any background. With the availability of depth information, background clutter and skin tone regions which are not part of the hand gesture can be removed improving the performance of any classification strategy. This article discusses a novel hand detection strategy based on Kinect camera by combining depth and colour image information. In the detection procedure, the Kalman filter is applied to the study to achieve a good detection result. The experiment results show this detection method is reliable and stable in the clutter background, and works well in various light conditions.