Robust Hand Detection and Tracking Based on Monocular Vision

Abstract

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.