Applying mean shift and motion detection approaches to hand tracking in sign language

Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. Several solutions such as the particle filter, kalman filter and dynamic programming tracking have been used, but they are complicated, time consuming and so expensive. The proposed method is so easy, fast, efficient and low cost. In the first step, the motion detection algorithm subtracts the previous frame from the current frame to obtain the changes between two images and white pixels (motion level) are detected by using the threshold level. Then the mean shift algorithm is applied for tracking the hand motion. Simulation results show this method is faster than two times to compared with the old common algorithms