Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.
Head pose estimation has a long history in computer vision. Murphy-Chutorian and Trivedi @cite_3 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 gaze 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. Prior work has shown that such a classification set is sufficient for the in-vehicle environment, even under rapidly shifting lighting conditions @cite_1 .
@cite_1 @cite_3
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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. 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.