摘要:疲劳驾驶是引发交通事故的一个主要原因，对驾驶员疲劳驾驶做出准确、有效的检测和预防，具有重要的社会意义.在研究比较了前人工作的基础上，设计了一种基于机器视觉，图像处理的驾驶员疲劳检测机制.首先将传来的连续帧图像（视频）利用Adaboost算法进行人脸检测，根据人脸"三庭五眼"的分布特征分割出大致的人眼区域.在人眼定位过程中，采用OSTU阈值分割，非线性点运算和积分投影等预处理消除眉毛，并利用模糊综合评价算法对眼睛矩形区域的长宽比、拟合椭圆面积、瞳孔黑色素所占比例这3个影响因子进行分析，判别出眼睛的睁开闭合状态.最后根据PERCLOS原理对驾驶员的疲劳状态做出检测.实验结果表明，所提算法能够准确地判别出眼睛的睁闭状态及对驾驶员的疲劳状态的检测，具有较高的准确性和实用性.



Abstract:Fatigue driving is one of the main causes of traffic accidents. It is important social significance to accurately and effectively detect and prevent the drivers' fatigue driving. Based on the research and comparison of previous work, this study designs a driver fatigue detection mechanism based on machine vision and image processing. First, the continuous frame image (video) is used to perform face detection using AdaBoost algorithm, and the approximate human eye area is segmented according to the distribution features of the human face "three courts and five holes". In the process of human eye positioning, the OSTU threshold segmentation, nonlinear point operations, and integral projections are used to eliminate eyebrows, and three influence factors, namely the fuzzy comprehensive evaluation algorithm for the ratio of the length to the width of the rectangular area of the eye, fitting the area of the ellipse, and the proportion of pupil melanin are analyzed to determine the open or closed state of the eye. Finally, according to the PERCLOS principle, the fatigue state of the driver is detected. The experimental results show that the proposed algorithm can accurately distinguish the open or closed state of the eyes, thus detect the driver's fatigue state with higher accuracy and practicability.



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