Driver Gaze Estimation Without Using Eye Movement

Abstract

Abstract—Automated estimation of the allocation of adriver’s visual attention may be a critical component offuture Advanced Driver Assistance Systems. In theory,vision-based tracking of the eye can provide a good estimateof gaze location. In practice, eye tracking from videois challenging because of sunglasses, eyeglass reflections,lighting conditions, occlusions, motion blur, and otherfactors. Estimation of head pose, on the other hand, isrobust to many of these effects, but cannot provide as fine-grained of a resolution in localizing the gaze. However,for the purpose of keeping the driver safe, it is sufficientto partition gaze into regions. In this effort, we proposea system that extracts facial features and classifies theirspatial configuration into six regions in real-time. Ourproposed method achieves an average accuracy of 91.4%at an average decision rate of 11 Hz on a dataset of 50drivers from an on-road study.Index Terms—Head pose estimation, gaze tracking, driverdistraction, driver assistance systems, on-road study.