Hand Gesture Detection and Segmentation Based on Difference Background Image with Complex Background

The video streams of backgrounds are frequently influenced by the background changes (e.g. illumination changes and changes due to adding or removing parts of the background). Further more, the quality of the foreground and the segmented image of hand gesture severely drops. We propose a novel method, which is based on difference background image between consecutive video frames, of using the ´3^sigma -principle´ of normal distribution for hand gesture detection to cope with the problem. The adaptive method of automatic threshold selection based on the method of maximal between-class variance is proposed for hand gesture segmentation to select optimal threshold. Experimentations show that the better images are obtained with complex background, no matter if the proportion of the hand gesture is high or low. Several experimental images are presented to support the validity of the method.