Hand detection and tracking using pixel value distribution model for multiple-camera-based gesture interactions

Abstract

We present a vision-based hand tracking system for gesture-based man-machine interactions and a statistical hand detection method. Our hand tracking system employs multiple cameras to reduce occlusion problems. Non-synchronous multiple observations enhance system scalability. In the system, users can manipulate a virtual scene by using predefined gesture commands. We propose a statistical method to detect hand regions in images using geometrical structures involved in the appearances of the target objects. Most conventional gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Our method can describe and recognize the appearances of hands based on geometrical structures. Experimental results show the effectiveness of our method.