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A surveillance method for driver's fatigue and distraction based on machine vision

机译:基于机器视觉的驾驶员疲劳和分心的监视方法

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摘要

Driver fatigue and distraction monitoring and warning system play an important role in reducing traffic accident and improving road safety. Using machine vision to monitor driver's driving state has become a research focus in safety driving assist system. A new algorithm is proposed which includes driver's face location based on gray variance, and segmentation of separate areas of facial organs using the projection curve pole position. The eye contour state can be got from OSTU calculation and contour extraction in each separate region. Calculation method of driver's facial rotation angle is presented based on the analysis of driver's head rotation. Different driver attention monitoring methods were used according to different head rotation angles. PERCLOS is used to monitor fatigue state when the driver face forward, while facial orientation is used to monitor distraction state when driver face deflection angle. Results show that the processing algorithm cost 32ms in average, so it has good real-time performance and high accuracy rate.
机译:司机疲劳和分散注意力监测和警告系统在减少交通事故和提高道路安全方面发挥着重要作用。使用机器愿景监控驾驶员的驾驶状态已成为安全驾驶辅助系统的研究重点。提出了一种新的算法,其包括基于灰度方差的驾驶员面部位置,以及使用投影曲线极位置的面部器官的单独区域的分割。可以从每个单独区域中的OSTU计算和轮廓提取来获得眼轮廓状态。基于驾驶员头旋转的分析,提出了驾驶员面旋转角度的计算方法。根据不同的头部旋转角度使用不同的驾驶员注意监测方法。 Perclos用于监测驾驶员面向前方时监测疲劳状态,而面部取向用于监测驾驶员面偏转角度时监测牵引状态。结果表明,加工算法平均成本为32ms,因此它具有良好的实时性能和高精度。

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