Hand posture recognition in video using multiple cues
Hand posture conveys profound information for computer vision applications, but the articulated hand structure and restraint capture condition cast a tough obstacle on practical implementation, especially in real time video. This paper presents a framework to recognize hand postures in consecutive video frames. Mixture of Gaussian skin/non skin models is constructed for hand region detection, followed by particle filter to track hand. Then a soft-decision scheme based on extended Histogram of Orientated gradient is proposed to refine the best posture region and recognize it from pre-defined posture set. Experimental result shows promising performance under various capture conditions.