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      Investigating the correspondence between driver head position and glance location


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          The relationship between a driver’s glance orientation and corresponding head rotation is highly complex due to its nonlinear dependence on the individual, task, and driving context. This paper presents expanded analytic detail and findings from an effort that explored the ability of head pose to serve as an estimator for driver gaze by connecting head rotation data with manually coded gaze region data using both a statistical analysis approach and a predictive (i.e., machine learning) approach. For the latter, classification accuracy increased as visual angles between two glance locations increased. In other words, the greater the shift in gaze, the higher the accuracy of classification. This is an intuitive but important concept that we make explicit through our analysis. The highest accuracy achieved was 83% using the method of Hidden Markov Models (HMM) for the binary gaze classification problem of (a) glances to the forward roadway versus (b) glances to the center stack. Results suggest that although there are individual differences in head-glance correspondence while driving, classifier models based on head-rotation data may be robust to these differences and therefore can serve as reasonable estimators for glance location. The results suggest that driver head pose can be used as a surrogate for eye gaze in several key conditions including the identification of high-eccentricity glances. Inexpensive driver head pose tracking may be a key element in detection systems developed to mitigate driver distraction and inattention.

          Most cited references28

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              Head pose estimation in computer vision: a survey.

              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.

                Author and article information

                PeerJ Comput Sci
                PeerJ Comput Sci
                PeerJ Computer Science
                PeerJ Inc. (San Francisco, USA )
                19 February 2018
                : 4
                : e146
                [1 ]AgeLab and New England University Transportation Center, Massachusetts Institute of Technology , Cambridge, MA, United States of America
                [2 ]Technical University of Munich , Munich, Germany
                [3 ]University of Augsburg , Augsburg, Germany
                [4 ]SAFER Vehicle and Traffic Safety Center, Chalmers , Göteborg, Sweden
                ©2018 Lee et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                : 1 August 2016
                : 23 January 2018
                Funded by: US DOT’s Region I New England University Transportation Center at MIT
                Funded by: The Santos Family Foundation
                Funded by: Toyota Class Action Settlement Safety Research and Education Program
                Support for this work was provided by the US DOT’s Region I New England University Transportation Center at MIT, The Santos Family Foundation and the Toyota Class Action Settlement Safety Research and Education Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Human-Computer Interaction
                Data Mining and Machine Learning

                head movements,glance classification,head-glance correspondence,driver distraction


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