基于人眼跟踪分析的疲劳驾驶检测的研究与实现

发布时间：2018-04-06 02:23

  本文选题：AdaBoost级联分类器　切入点：模板匹配　出处：《东北大学》2014年硕士论文

【摘要】：随着汽车保有量的增加,我国交通安全问题日益突出,由驾驶员疲劳驾驶造成的交通事故越来越多,现已成为交通事故发生的主要因素之一。由此可见,研究并实现疲劳检测相关算法对预防交通事故的发生有着重大的现实意义。本文对国内外有关驾驶员疲劳检测的相关技术进行了系统分析,最终选取AdaBoost定位算法和模板匹配跟踪算法,并做了进一步研究。在定位阶段,首先对原图像进行预处理,主要包括理想光照条件下的图像灰度化、直方图均衡化与非理想光照条件下的图像亮度对比度变换,然后应用人脸AdaBoost级联分类器进行人脸定位,并对检测到的所有的人脸进行判断,得出真正的驾驶员脸部范围,进而在此范围内应用人眼AdaBoost级联分类器进行人眼定位,并通过自适应阈值判断得出正确的人眼,从而最终得到人眼跟踪所用的模板;在跟踪阶段,根据连续两帧人眼在水平方向和垂直方向的位移预测出人眼区域,并应用模板匹配对人眼进行实时跟踪,但传统的模板匹配算法常常由于累积误差和人眼的眨动导致跟踪丢失,所以本文提出在模板匹配的基础上进行人眼轮廓提取,并根据人眼轮廓对人眼模板进行更新,从而解决了跟踪丢失的问题；在疲劳评测阶段,根据提取的人眼状态参数统计眨眼频率和计算PERCLOS值,最终得到人眼在不同时刻的特征,根据眼睛状态判断驾驶员疲劳程度。为了避免目标跟踪丢失,保证跟踪的准确性和实时性,跟踪阶段还需对人眼进行重定位判断,在跟踪出错或丢失时及时的进行重定位。本文在PC机上采用C#编程语言,使用VS2008开发环境并基于OpenCV计算机视觉库仿真实现了驾驶员疲劳评测算法。对理想条件下不同光照、不同速度、不同旋转角度和非理想条件下不同颠簸程度、光照突变等情况的实时性和准确性进行测试。根据测试结果分析可知,在非极端情况下对人脸、眼睛定位与眼睛跟踪算法在各个阶段均能实时准确地实现,而在极端情况下实验结果虽有改进但是效果不是特别明显。驾驶员通常驾车环境都是出于非极端条件下,从而验证了本文所用的算法适用于驾驶员疲劳检测。
[Abstract]:With the increase of vehicle ownership, traffic safety problems become more and more prominent in China. More and more traffic accidents caused by drivers' fatigue driving have become one of the main factors of traffic accidents.Therefore, it is of great practical significance to study and implement fatigue detection algorithms to prevent traffic accidents.In this paper, the related techniques of driver fatigue detection at home and abroad are systematically analyzed. Finally, AdaBoost location algorithm and template matching tracking algorithm are selected, and further research is done.In the localization stage, the original image is preprocessed, which includes image grayness under ideal illumination, histogram equalization and image brightness contrast transformation under non-ideal illumination.Then face location is carried out by using the face AdaBoost cascade classifier, and all the faces detected are judged, and the real face range of the driver is obtained, and then the human eye AdaBoost cascade classifier is used to locate the human eye in this range.The correct human eye is obtained by adaptive threshold judgment, and the template for eye tracking is finally obtained. In the tracking stage, the human eye region is predicted according to the horizontal and vertical displacement of the human eye in two successive frames.Template matching is used to track human eyes in real time, but the traditional template matching algorithms often lose track due to accumulated errors and blink of human eyes, so this paper proposes to extract human eye contour on the basis of template matching.According to the human eye profile, the human eye template is updated to solve the problem of tracking loss, and in the fatigue evaluation stage, the blink frequency and the PERCLOS value are calculated according to the extracted human eye state parameters, and the characteristics of the human eye at different times are finally obtained.Judge the driver's fatigue according to the state of the eye.In order to avoid the loss of target tracking and ensure the accuracy and real-time of tracking, it is necessary to reposition the human eyes in the tracking stage, and to relocate in time when the tracking is wrong or lost.In this paper, we use C # programming language on PC, use VS2008 development environment and realize driver fatigue evaluation algorithm based on OpenCV computer vision library simulation.The real time and accuracy of different illumination, different speed, different rotation angle, different turbulence degree and illumination sudden change in ideal condition were tested.According to the analysis of the test results, the eye localization and eye tracking algorithms can be realized in real time and accurately in the non-extreme cases, but in extreme cases the experimental results are improved, but the effect is not particularly obvious.Drivers usually drive under non-extreme conditions, which verifies that the algorithm proposed in this paper is suitable for driver fatigue detection.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U495;TP391.41
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本文编号：1717559