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Spatio-temporal features for the automatic control of driver drowsiness state and lack of concentration

机译：时空特性，用于自动控制驾驶员的睡意状态和注意力不集中

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摘要

Driver fatigue is one of the leading causes of road accidents. It affects the mental vigilance of the driver and reduces his personal capacity to drive a vehicle in full safety. These factors increase the risk of human errors which could involve deaths and wounds. Consequently, the development of an automatic system, which controls the driver fatigue and prevents him from accidents in advance, has received a growing interest. In this work, we have proposed a fusion system for drowsiness detection based on blinking measurement and the 3D head pose estimation. We have studied the driver's eye behaviors by analysing a non-stationary and non-linear signal and we estimate the head rotation in the three directions Yaw, Pitch, and Roll by exploiting only three interest points of the face. Our suggested system of fusion presents three levels of drowsiness: awake, tired, and very tired. This system is evaluated by both DEAP and Miracl HB databases. The evaluation shows many promising results and shows the effectiveness of the suggested approach.
机译：驾驶员疲劳是道路交通事故的主要原因之一。这会影响驾驶员的心理警觉，并降低其驾驶人的能力以完全安全地驾驶车辆。这些因素增加了可能导致死亡和伤亡的人为错误的风险。因此，控制驾驶员疲劳并提前防止驾驶员发生事故的自动系统的开发引起了越来越多的兴趣。在这项工作中，我们提出了一种基于眨眼测量和3D头部姿势估计的睡意检测融合系统。我们通过分析非平稳和非线性信号研究了驾驶员的眼睛行为，并仅通过利用面部的三个兴趣点来估计了头部在Yaw，Pitch和Roll三个方向上的旋转。我们建议的融合系统呈现出三种睡意状态：清醒，疲倦和非常疲倦。该系统由DEAP和Miracl HB数据库评估。评估显示了许多有希望的结果，并表明了所建议方法的有效性。

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