Automated estimation of the allocation of a driver's visual attention may be a critical component of future Advanced Driver Assistance Systems. In theory, vision-based tracking of the eye can provide a good estimate of gaze location. In practice, eye tracking from video is challenging because of sunglasses, eyeglass reflections, lighting conditions, occlusions, motion blur, and other factors. Estimation of head pose, on the other hand, is robust 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 sufficient to partition gaze into regions. In this effort, we propose a system that extracts facial features and classifies their spatial configuration into six regions in real-time. Our proposed method achieves an average accuracy of 91.4 at an average decision rate of 11 Hz on a dataset of 50 drivers from an on-road study.
Head pose estimation has a long history in computer vision. Murphy-Chutorian and Trivedi @cite_5 describe 74 published and tested systems from the last two decades. Generally, each approach makes one of several assumptions that limit the general applicability of the system in driver state detection. These assumptions include: (1) the video is continuous, (2) initial pose of the subject is known, (3) there is a stereo vision system available, (4) the camera has frontal view of the face, (5) the head can only rotate on one axis, (6) the system only has to work for one person. While the development of a set of assumptions is often necessary for the classification of a large number of possible poses, our approach skips the head pose estimation step (i.e., the computation of a vector in 3D space modeling the orientation of the head) and goes straight from the detection of a facial features to a classification of gaze to one of six glance regions. We believe that such a classification set is sufficient for the in-vehicle environment where the overarching goal is to assess if the driver is distracted or inattentive to the driving context.
@cite_5
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The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.