Estimating Gaze Direction of Vehicle Drivers using a Smartphone Camera

Meng-Che Chuang, Raja Bala, Edgar A. Bernal, Peter Paul, Aaron Burry; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2014, pp. 165-170

Abstract


Many automated driver monitoring technologies have been proposed to enhance vehicle and road safety. Most existing solutions involve the use of specialized embedded hardware, primarily in high-end automobiles. This paper explores driver assistance methods that can be implemented on mobile devices such as a consumer smartphone, thus offering a level of safety enhancement that is more widely accessible. Specifically, the paper focuses on estimating driver gaze direction as an indicator of driver attention. Input video frames from a smartphone camera facing the driver are first processed through a coarse head pose direction. Next, the locations and scales of face parts, namely mouth, eyes, and nose, define a feature descriptor that is supplied to an SVM gaze classifier which outputs one of 8 common driver gaze directions. A key novel aspect is an in-situ approach for gathering training data that improves generalization performance across drivers, vehicles, smartphones, and capture geometry. Experimental results show that a high accuracy of gaze direction estimation is achieved for four scenarios with different drivers, vehicles, smartphones and camera locations.

Related Material


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[bibtex]
@InProceedings{Chuang_2014_CVPR_Workshops,
author = {Chuang, Meng-Che and Bala, Raja and Bernal, Edgar A. and Paul, Peter and Burry, Aaron},
title = {Estimating Gaze Direction of Vehicle Drivers using a Smartphone Camera},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2014}
}