Head Pose Estimation Based on Head Tracking and the Kalman Filter

Abstract

In this paper, we propose a head pose estimation method which combines a texture-based head tracking method and the Kalman filter. The texture-based tracking method first estimates the head pose in the current frame by recovering the relative head motion between consequence frames. The Kalman filter predicts the head pose in the next frame, which can help the tracking method to recover motion from the predicted pose. Our method has been tested on a real video sequence. The experiment results show it successfully tracks a head and improve the efficiency of the tracking method.