Wakefulness State Estimation Method Using Eye Movement and Deep Learning

Yutaka Tsuzuki, Naoya Ishida, Yuki Nagatsu, H. Hashimoto
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Abstract

In recent years, the number of traffic accidents caused by drivers falling asleep has increased. Many studies have reported that drowsiness occurs while driving, resulting in a need of a system to detect driver drowsiness. In this study, we use a camera sensor that can sense the state of a person while driving a car without any physical constraints and without awareness of the device. We also estimate the wakefulness state using deep learning and eye movements to detect any increase in wakefulness due to drowsiness. This estimation method is found to be highly accurate.
基于眼动和深度学习的清醒状态估计方法
近年来,由司机睡着引起的交通事故数量有所增加。许多研究报告说,在驾驶时发生困倦,因此需要一个系统来检测驾驶员的困倦。在这项研究中,我们使用了一种摄像头传感器,它可以在没有任何物理限制和设备意识的情况下感知人在驾驶汽车时的状态。我们还使用深度学习和眼球运动来估计清醒状态,以检测由于困倦而导致的清醒程度的增加。这种估计方法被证明是非常准确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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