Stereo vision-based driver head pose estimation

Abstract

In conjunction with the advancing development of driver assistance systems, driver observation becomes increasingly important. This paper proposes a new approach for driver's head pose estimation. With a stereo camera mounted in a realistic position on top of the center stack the system continuously tracks the orientation of the driver's head in realtime (25fps) using solely 3-D information. The systems processing chain comprises separate modules for head separation, pose estimation and pose tracking. Head separation employes a Bayesian modeling approach for robust head-torso separation. The pose estimation module uses Synchronized Submanifold Embedding (SSE), a nonlinear regression method, which includes a dimensionality reduction, a k-nearest neighbor search and a barycentric coordinate estimation. The tracking module estimates angular velocity of the head, using an Extended Kalman Filter (EKF) in quaternion space. Comprehensive experiments show, that the proposed system achieves high accuracy from a non-central camera position. Since the approach does not rely on facial feature points the system handles large pose variations and is not disturbed by (sun)glasses.