HyHOPE: Hybrid Head Orientation and Position Estimation for vision-based driver head tracking

Abstract

Driver distraction and inattention are prominent causes of automotive collisions. To enable driver assistance systems to address these problems, we require new sensing approaches to infer a driverpsilas focus of attention. In this paper, we present a new 3D tracking algorithm and integrate it into HyHOPE, a real-time (30 fps) hybrid head orientation and position estimation system for driver head tracking. With a single video camera, the system continuously tracks the head in six degrees-of-freedom, initializing itself automatically with separate modules for head detection and head pose estimation. The tracking module provides a fine estimate of the 3D motion of the head, using a new appearance-based algorithm for 3D model tracking by particle filtering in an augmented reality environment. We describe our implementation, which utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real-time. To quantitatively evaluate the accuracy of our system, we compare its estimation results to a marker-based cinematic motion capture system installed in an automotive testbed. We evaluate the system on real daytime and nighttime drives with drivers of varying ages, race, and sex.