Robust Hand Detection and Tracking Based on Monocular Vision

Vision based hand tracking is an important area of human machine interaction (HCI) and virtual reality techniques. However, it is still a great challenge. Due to the complicated environment and various hand appearances, it is difficult to realize stable and long-term tracking. In this paper, we proposed an effective approach to detect and track hand with a normal webcam. An integrating multi-cue detector with skin color and hand classifier is used to initialize the tracking region and find appearance information during tracking. Also a median flow tracker is integrated which utilizing the motion information to enhance the accuracy in short-term. We realize an automatic system of stable and long-term hand tracking, which can detect and track pre-trained frontal view hand gesture. Experiments show the good performance of our approach.