基于眼部特征的疲劳驾驶实时检测算法研究
摘要
如今,汽车在世界范围内已经成为一种最普遍的交通工具。据德国一家汽车市场调研机构预测,全球汽车(包括个人用车和商用车)保有量2010年就将突破10亿辆,这种形势直接给世界的交通安全事业带来了极大的压力和挑战。疲劳驾驶已经成为了全球交通事故发生的主要诱因之一。
在当前的疲劳驾驶检测技术领域,随着科技的不断进步,计算机视觉、图像处理和模式识别技术得到了进一步的发展和完善。基于驾驶员脸部特征的非接触式疲劳检测算法的研究和疲劳预警系统的开发已经成为了主流之一。
本文针对这一背景,在自然光下利用视频图像处理技术研究了驾驶员人脸和眼睛状态的检测算法,参考PERCLOS疲劳检测准则研究出了一种比较准确的疲劳检测方法,从而实现了对驾驶疲劳的检测和预警。
本文所做的主要工作包括:
(1)针对驾驶员人脸的检测,利用YCbCr和HSV双色彩空间的肤色信息和Adaboost分类器的融合研究出了一种具有鲁棒性好、计算速度快的驾驶员人脸检测与定位算法。
(2)在人脸检测与定位的基础上,基于由粗到精多级筛选的思想,利用人眼区域模板匹配算法和Hough变换瞳孔圆检测的算法实现了对人眼的定位和眼睛开闭状态认定,为后续的疲劳判断打下了理论基础。
(3)在判定了人眼状态的条件下,通过对驾驶疲劳的认真分析,参照PERCLOS准则设计出了一种基于阈值分割思想的疲劳检测方法。
(4)利用C语言对整个算法进行了源码实现,借助OpenCV开发平台完成了对整个检测算法的效果验证。
经过实验室模拟试验表明,本文研究的这种基于眼部特征的疲劳驾驶实时检测算法具有比较好的正确率和鲁棒性,为以后更深入的研究和算法的硬件实现打下了基础。
Today, the car in the world has become one of the most common means of transport. According to a German automotive market researching institution predicted that Global vehicles (including personal cars and commercial vehicles) holdings in 2010 will surpass 1 billion. This situation has brought great pressures and challenges to the world's traffic safety directly. Fatigue driving has become one of the main incentives of the world's traffic accidents.
In the current driver fatigue detection field, as technology advances, computer vision, image processing and pattern recognition technology has been further developed and improved. The research based on driver facial features, non-contact fatigue detection algorithm and development of fatigue warning systems have become one of the mainstream.
In view of this background, this paper developed a detection algorithm based on driver's face and eye state, with the video image processing technology in natural light. Referring to PERCLOS criteria of fatigue testing, it developed a accurate fatigue detection method, and achieved the driver fatigue detection and early warning.
This paper included the following contents:
(1)For the driver's face detection, it developed a driver's face detection and localization algorithm which has good robustness and fast calculation. The algorithm syncretized the skin color information based on YCbCr & HSV color space and Adaboost classifier.
(2)In the basis of human face detection and location, it achieved pupil of human eyes orientation and the state of eyes identified, using the area of human eye template matching algorithm and the Hough transform circle detection algorithm. The method based on the from coarse to fine multi-level filtering idea. It had laid a theoretical basis for follow-up to determine the fatigue.
(3)In the determination of the conditions of the state of the human eyes, through careful analysis of fatigue on driving, referring to PERCLOS criteria it designed a fatigue detection method based on the idea of threshold-segmentation.
(4)It implemented the entire algorithm with C language source code. Using OpenCV development platform, it completed the effect of the whole detection algorithm validation.
Through laboratory simulations, it showed that based on feature of the eyes driving fatigue real-time detection algorithm has better accuracy and robustness. It laid the foundation for more in-depth researching of algorithm and hardware implementation.
在当前的疲劳驾驶检测技术领域,随着科技的不断进步,计算机视觉、图像处理和模式识别技术得到了进一步的发展和完善。基于驾驶员脸部特征的非接触式疲劳检测算法的研究和疲劳预警系统的开发已经成为了主流之一。
本文针对这一背景,在自然光下利用视频图像处理技术研究了驾驶员人脸和眼睛状态的检测算法,参考PERCLOS疲劳检测准则研究出了一种比较准确的疲劳检测方法,从而实现了对驾驶疲劳的检测和预警。
本文所做的主要工作包括:
(1)针对驾驶员人脸的检测,利用YCbCr和HSV双色彩空间的肤色信息和Adaboost分类器的融合研究出了一种具有鲁棒性好、计算速度快的驾驶员人脸检测与定位算法。
(2)在人脸检测与定位的基础上,基于由粗到精多级筛选的思想,利用人眼区域模板匹配算法和Hough变换瞳孔圆检测的算法实现了对人眼的定位和眼睛开闭状态认定,为后续的疲劳判断打下了理论基础。
(3)在判定了人眼状态的条件下,通过对驾驶疲劳的认真分析,参照PERCLOS准则设计出了一种基于阈值分割思想的疲劳检测方法。
(4)利用C语言对整个算法进行了源码实现,借助OpenCV开发平台完成了对整个检测算法的效果验证。
经过实验室模拟试验表明,本文研究的这种基于眼部特征的疲劳驾驶实时检测算法具有比较好的正确率和鲁棒性,为以后更深入的研究和算法的硬件实现打下了基础。
Today, the car in the world has become one of the most common means of transport. According to a German automotive market researching institution predicted that Global vehicles (including personal cars and commercial vehicles) holdings in 2010 will surpass 1 billion. This situation has brought great pressures and challenges to the world's traffic safety directly. Fatigue driving has become one of the main incentives of the world's traffic accidents.
In the current driver fatigue detection field, as technology advances, computer vision, image processing and pattern recognition technology has been further developed and improved. The research based on driver facial features, non-contact fatigue detection algorithm and development of fatigue warning systems have become one of the mainstream.
In view of this background, this paper developed a detection algorithm based on driver's face and eye state, with the video image processing technology in natural light. Referring to PERCLOS criteria of fatigue testing, it developed a accurate fatigue detection method, and achieved the driver fatigue detection and early warning.
This paper included the following contents:
(1)For the driver's face detection, it developed a driver's face detection and localization algorithm which has good robustness and fast calculation. The algorithm syncretized the skin color information based on YCbCr & HSV color space and Adaboost classifier.
(2)In the basis of human face detection and location, it achieved pupil of human eyes orientation and the state of eyes identified, using the area of human eye template matching algorithm and the Hough transform circle detection algorithm. The method based on the from coarse to fine multi-level filtering idea. It had laid a theoretical basis for follow-up to determine the fatigue.
(3)In the determination of the conditions of the state of the human eyes, through careful analysis of fatigue on driving, referring to PERCLOS criteria it designed a fatigue detection method based on the idea of threshold-segmentation.
(4)It implemented the entire algorithm with C language source code. Using OpenCV development platform, it completed the effect of the whole detection algorithm validation.
Through laboratory simulations, it showed that based on feature of the eyes driving fatigue real-time detection algorithm has better accuracy and robustness. It laid the foundation for more in-depth researching of algorithm and hardware implementation.
引文
[1] Viola P, Jones M. J. Robust Real-Time Face Detection. International Journal of Computer Vision 57(2),137-154,2004
[2] Grace R, Byrne VE, Legrand JM, et al. A machine vision based drowsy driver detection system for heavy vehicles. Proceedings of The Ocular Measures of Driver Alertness Conference,1999:75-86
[3] Viola P. R Rapid object detection using a Boosted cascade of simple features. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, pp:511~518,2001
[4] Ellen MA, Grace R, Steinfeld A user-centered drowsy-driver detection and warning system. Proceedings of ACM Designing User Experiences, 2003:109-113
[5] Viola P, Jones M. Robust real time object detection Technical Report. CRL 2001/01, Compaq Cambridge Research Laboratory 2001.
[6] Freund Y, Sehapire R.E, A decision theoretic generalization of online learning and an application to boosting. Journal of Computer and System Science, 1997,55(1),119-139
[7]翁茂荣,李强,花彩霞.机动车驾驶员疲劳检测系统的研究现状及发展趋势[J].浙江工贸职业技术学院学报,2006,6(1):52-56
[8]李冯,姚莉秀,杨杰等.复杂背景下的彩色图像人脸检测[J].上海交通大学学报,2006,40(5):778-782
[9]王欣,赵巍,姚利增.基于肤色的人脸检测方法中的常见色彩模型分析[J].黑龙江科技信息,2008,11:36
[10]李刚,高政.人脸检测技术研究与发展[J].计算机与现代化,2003,4:7-9,12
[11]袁翔,黄博学,夏晶晶.疲劳驾驶检测方法研究现状[J].公路与汽运,2007,120(3):51-54
[12]徐庆,石跃祥等.基于改进YUV空间的人脸检测方法[J].计算机工程与应用,2008,44(34):158-162
[13] Voila P,Jones M.Robust real -time face diction [J]. Inter-national Journal of Computer Vision.2004,57(2):137-154
[14]丁兴林,王明,张立材.基于PERCLOS的驾驶员眼睛状态识别.微计算机信息,2007
[15]夏思宇,李久贤等.一种改进的自适应肤色检测算法[J].数据采集与处理,2006,2:174-178
[16] Papageorgiou C.Ore n M,Poggio A general framework for object detection [R] .In Internation Conference on Computer Vision,1998
[17]许海柱,唐琎 ,王力.一种新的图像中的人眼检测算法[J].计算机应用研究,2008,25(7):2223-2224,2227.
[18]李月敏,陈杰,高文,尹宝才.快速人脸检测技术综述[J]. http://www.image2003. com/paper/down/200551610042200.pdf
[19]周鹏.疲劳事故隐患消除技术与方法[J].汽车电器,1998,(8):27-30
[20]陈伟.疲劳驾驶:法国人的防范之策[J].社会发展,2000,(10):43-44
[21]王基帆,钱艺,童卫青.基于AdaBoost算法的人眼定位[J].现代计算机,2009,9:73-75
[22]郭磊,王秋光. AdaBoost人脸检测算法研究及OpenCV实现[J].哈尔滨理工大学学报,2009,14(5):123-126
[23]范一峰,颜志英.一个基于AdaBoost的快速人脸检测系统的实现[J].计算机与现代化,2008,12:149-152
[24]孙艳秋.一种简单快速的人眼定位方法[J].赤峰学院学报(自然科学版) ,2008,24(3):116-118
[25]张杰,杨晓飞,赵瑞莲.基于Hough变换圆检测的人眼精确定位方法[J].计算机工程与应用,2005,27:43-44,71
[26]黎云汉,朱善安. Hough变换在眼睛特征提取中的应用[J].浙江大学学报(工学版),2008,42(7):1164-1168
[27]曹菊英,赵跃龙.基于水平投影和Hough查找圆法的人眼状态识别研究[J].科学技术与工程,2007,7(6):1270-1272
[28]胡涛,张兵.基于形状特征的人眼状态判断[J].计算机工程与应用. 2009,45(5):203-206
[29]于兴玲,王民,张立材.基于PERCLOS的驾驶员眼睛状态检测方法[J].微计算机信息,2007,24(5-2):251-253
[30]万玉丽,谢金法.基于PERCLOS的驾驶员疲劳检测方法的实现算法[J].农业装备技术,2009,35(2):35-28
[31]许世峰,曾义.基于AdaBoost算法的人眼状态检测[J].计算机仿真,2007,24(7):214-216,341
[32]刘谨奕,张冬茉,申丽萍.一种基于模糊逻辑的实用睡意检测方法[J].计算机应用与软件,2008,25(4):182-184
[33]翁茂荣.基于眼睛状态的驾驶员疲劳模糊识别研究[J].计算机仿真.2008,25(6):244-247,273
[34]李佩林,何翠群,李志春.驾驶员疲劳状况检测中眼睛定位方法的比较与分析[J].森林工程,2008,24(5):35-38.
[35]蒋建国,刘扬,詹曙等.灰度视频序列中驾驶员疲劳实时检测方法[J].合肥工业大学学报(自然科学版),2008,31(9):1424-1427,1442
[36]余甜甜,唐普英.基于模板匹配和遗传算法的人眼定位[J].计算机仿真,2007,24(4):200-201,239
[37]曹炜,汪丰,周平.基于机器视觉的人眼定位算法研究[J].交通与计算机,2007,25(6):34-36,40
[38]江水郎,杨明.面向驾驶员疲劳检测的双空间人眼定位方法[J].计算机工程,2008,34(24):180-182
[39]刘艺,龚卫国,李伟红.双层结构Adaboost健壮分类器用于人眼精确定位[J].计算机应用,2008,28(3):801-803
[40]杨凌曦,徐建闽. Adaboost人脸检测算法在驾驶员疲劳检测系统中的实现[J].交通与计算机,2008,26(143):140-143
[41]董文会,吴晓娟,徐祗军.基于图像处理的驾驶员疲劳检测方法[J].计算机应用与软件,2006,23(12):70-71,77
[42]刘瑞祯,于仕琪. OpenCV教程-基础篇[M].北京:北京航空航天大学出版社,2007,6
[43]刘宏,邱健雄,谢中科. C++程序设计教程[M].武汉:武汉大学出版社,2005
[2] Grace R, Byrne VE, Legrand JM, et al. A machine vision based drowsy driver detection system for heavy vehicles. Proceedings of The Ocular Measures of Driver Alertness Conference,1999:75-86
[3] Viola P. R Rapid object detection using a Boosted cascade of simple features. In: Proc IEEE Conference on Computer Vision and Pattern Recognition, pp:511~518,2001
[4] Ellen MA, Grace R, Steinfeld A user-centered drowsy-driver detection and warning system. Proceedings of ACM Designing User Experiences, 2003:109-113
[5] Viola P, Jones M. Robust real time object detection Technical Report. CRL 2001/01, Compaq Cambridge Research Laboratory 2001.
[6] Freund Y, Sehapire R.E, A decision theoretic generalization of online learning and an application to boosting. Journal of Computer and System Science, 1997,55(1),119-139
[7]翁茂荣,李强,花彩霞.机动车驾驶员疲劳检测系统的研究现状及发展趋势[J].浙江工贸职业技术学院学报,2006,6(1):52-56
[8]李冯,姚莉秀,杨杰等.复杂背景下的彩色图像人脸检测[J].上海交通大学学报,2006,40(5):778-782
[9]王欣,赵巍,姚利增.基于肤色的人脸检测方法中的常见色彩模型分析[J].黑龙江科技信息,2008,11:36
[10]李刚,高政.人脸检测技术研究与发展[J].计算机与现代化,2003,4:7-9,12
[11]袁翔,黄博学,夏晶晶.疲劳驾驶检测方法研究现状[J].公路与汽运,2007,120(3):51-54
[12]徐庆,石跃祥等.基于改进YUV空间的人脸检测方法[J].计算机工程与应用,2008,44(34):158-162
[13] Voila P,Jones M.Robust real -time face diction [J]. Inter-national Journal of Computer Vision.2004,57(2):137-154
[14]丁兴林,王明,张立材.基于PERCLOS的驾驶员眼睛状态识别.微计算机信息,2007
[15]夏思宇,李久贤等.一种改进的自适应肤色检测算法[J].数据采集与处理,2006,2:174-178
[16] Papageorgiou C.Ore n M,Poggio A general framework for object detection [R] .In Internation Conference on Computer Vision,1998
[17]许海柱,唐琎 ,王力.一种新的图像中的人眼检测算法[J].计算机应用研究,2008,25(7):2223-2224,2227.
[18]李月敏,陈杰,高文,尹宝才.快速人脸检测技术综述[J]. http://www.image2003. com/paper/down/200551610042200.pdf
[19]周鹏.疲劳事故隐患消除技术与方法[J].汽车电器,1998,(8):27-30
[20]陈伟.疲劳驾驶:法国人的防范之策[J].社会发展,2000,(10):43-44
[21]王基帆,钱艺,童卫青.基于AdaBoost算法的人眼定位[J].现代计算机,2009,9:73-75
[22]郭磊,王秋光. AdaBoost人脸检测算法研究及OpenCV实现[J].哈尔滨理工大学学报,2009,14(5):123-126
[23]范一峰,颜志英.一个基于AdaBoost的快速人脸检测系统的实现[J].计算机与现代化,2008,12:149-152
[24]孙艳秋.一种简单快速的人眼定位方法[J].赤峰学院学报(自然科学版) ,2008,24(3):116-118
[25]张杰,杨晓飞,赵瑞莲.基于Hough变换圆检测的人眼精确定位方法[J].计算机工程与应用,2005,27:43-44,71
[26]黎云汉,朱善安. Hough变换在眼睛特征提取中的应用[J].浙江大学学报(工学版),2008,42(7):1164-1168
[27]曹菊英,赵跃龙.基于水平投影和Hough查找圆法的人眼状态识别研究[J].科学技术与工程,2007,7(6):1270-1272
[28]胡涛,张兵.基于形状特征的人眼状态判断[J].计算机工程与应用. 2009,45(5):203-206
[29]于兴玲,王民,张立材.基于PERCLOS的驾驶员眼睛状态检测方法[J].微计算机信息,2007,24(5-2):251-253
[30]万玉丽,谢金法.基于PERCLOS的驾驶员疲劳检测方法的实现算法[J].农业装备技术,2009,35(2):35-28
[31]许世峰,曾义.基于AdaBoost算法的人眼状态检测[J].计算机仿真,2007,24(7):214-216,341
[32]刘谨奕,张冬茉,申丽萍.一种基于模糊逻辑的实用睡意检测方法[J].计算机应用与软件,2008,25(4):182-184
[33]翁茂荣.基于眼睛状态的驾驶员疲劳模糊识别研究[J].计算机仿真.2008,25(6):244-247,273
[34]李佩林,何翠群,李志春.驾驶员疲劳状况检测中眼睛定位方法的比较与分析[J].森林工程,2008,24(5):35-38.
[35]蒋建国,刘扬,詹曙等.灰度视频序列中驾驶员疲劳实时检测方法[J].合肥工业大学学报(自然科学版),2008,31(9):1424-1427,1442
[36]余甜甜,唐普英.基于模板匹配和遗传算法的人眼定位[J].计算机仿真,2007,24(4):200-201,239
[37]曹炜,汪丰,周平.基于机器视觉的人眼定位算法研究[J].交通与计算机,2007,25(6):34-36,40
[38]江水郎,杨明.面向驾驶员疲劳检测的双空间人眼定位方法[J].计算机工程,2008,34(24):180-182
[39]刘艺,龚卫国,李伟红.双层结构Adaboost健壮分类器用于人眼精确定位[J].计算机应用,2008,28(3):801-803
[40]杨凌曦,徐建闽. Adaboost人脸检测算法在驾驶员疲劳检测系统中的实现[J].交通与计算机,2008,26(143):140-143
[41]董文会,吴晓娟,徐祗军.基于图像处理的驾驶员疲劳检测方法[J].计算机应用与软件,2006,23(12):70-71,77
[42]刘瑞祯,于仕琪. OpenCV教程-基础篇[M].北京:北京航空航天大学出版社,2007,6
[43]刘宏,邱健雄,谢中科. C++程序设计教程[M].武汉:武汉大学出版社,2005