Hand gesture tracking system using Adaptive Kalman Filter

This paper introduces a hand tracking system in unconstrained environment. The system consists of two main stages which are initialization and tracking. In initialization, hand region is first detected by combining motion and skin color pixels. A region of interest (ROI) is then created around the detected hand region. In tracking stage, skin and motion pixels are scanned around top, left and right corners of the ROI to detect the moving hand in consecutive video frames. These pixels are used to actually measure the ROI position and fed into measurement update of Adaptive Kalman Filter (AKF) operation. The process noise covariance and measurement noise covariance of AKF are adjusted adaptively by applying weighting factor based on acceleration threshold value. The experimental result shows the proposed method has the robust ability to track the moving hand under real life scenarios at speed 45 fps with average 97.83% tracking rate.