基于眼睑运动和凝视方向的驾驶疲劳检测
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摘要
目前交通事故中相当一部分是由司机疲劳驾驶引起的,因此,对驾驶员的疲劳状态进行检测,减少此类事故的发生,有着积极的现实意义。本文对疲劳检测的算法进行了一定研究,通过研究司机的眼睑运动即眼睛闭合状态和凝视方向来综合评判司机的疲劳程度。
本文主要做了以下几个方面的工作:
(1)为了提高人眼定位的准确度和速度,本文采用ASM(主动形状模型)进行人眼的粗定位,然后单独对人眼区域进行肤色分割,实现人眼细定位,细定位的结果是得到5个人眼特征点。实验表明该方法速度快,准确率高。
(2)为了达到实时性要求,定位之后就要进行人眼的跟踪,本文提出了一种新颖的眼睛跟踪方法,即综合Kalman滤波和ASM算法来进行人眼跟踪。整个跟踪过程分为两个阶段:首先根据上一帧图像中人眼的位置运用Kalman滤波预测在下一帧中人眼的位置,然后以此作为ASM的初始状态,跟踪人眼。
(3)根据跟踪到的人眼进行眼睛闭合状态识别和凝视方向计算。本文提出新颖的方法即对人眼区域的肤色分割图计算人眼黑色块面积的方法来计算人眼闭合程度;对于凝视方向,本文通过获取的5个人眼特征点以几何的方法进行计算,将凝视方向分为左中右,上中下共9个方向以进行区分,方法简单、快速、效果好。
(4)根据单一参数评判疲劳程度效果并不是很好,本文综合人眼闭合程度和凝视方向进行司机驾驶疲劳的检测,建立新的评判规则。
本文的创新点主要有以下三个方面:
(1)虽然以前也有人在疲劳检测的时候提到人眼凝视方向,但并没有真正指明如何计算人眼凝视方向,并提出评判规则。本文用图像处理的方法计算人眼凝视方向,并与PERCLOS一起建立疲劳检测的评判规则。
(2)在人眼定位与跟踪算法上,本文将ASM与基于肤色的方法相结合,速度快、效果好。
(3)相对于通过模板匹配的方法计算人眼闭合程度,本文所采用的通过计算人眼黑色块的面积的方法更加简洁明了,可靠性更高。
Nowadays, many traffic accidents take place because of the fatigued drivers. Therefore, it is significant to detect drivers' fatigue state to decrease these traffic accidents. In this thesis, fatigue detection methods have been studied and driver's fatigue level has been estimated according to his eyelid movement (eye state) and gaze direction synthetically.
In this thesis four facets work has been done.
Firstly, in order to improve the veracity and speed of eye detection, ASM has been used for coarse eye detection and then segments the eye region alone for careful eye detection, the purpose of careful eye detection is to obtain five eye feature points. The Experiments indicate that this method is fast and accurate.
Secondly, for the purpose of real time ability, this thesis proposes a novel eyes tracking method after the detecting of the eyes, which synthesizes Kalman and ASM arithmetic. The whole tracking process is separated into two steps: first of all, using Kalman to estimate the location of the eyes in the next frame according to the location in the previous frame, and then take it as the initial state of the ASM to track the eyes.
Thirdly, calculates eye state and gaze direction according to the results of eye detection. This thesis proposes a novel method to get the eye state by calculating the eye black block area in segmentation image. Gaze direction has been divided into nine parts, each part can be calculated by geometrical method according to five eye feature points, which has several advantages such as briefness、celerity、better effect.
Fourthly, using single parameter to estimate fatigue level doesn't work well, so this thesis combines eye status and gaze direction to create new formulas for driver fatigue detection.
This thesis has three innovations:
Though gaze direction was proposed before in fatigue detection, the way to calculate gaze direction didn't designate clearly. The method of image processing has been used to calculate the gaze direction and then createfatigue detection formulas with PERCLOS.
This thesis proposes a novel method to combine ASM and skin color segmentation for eye detecting and tracking, which is faster and better than others.
Compared with the method of using template matching to calculate eye status, the method this thesis used is more succinct and credible, which calculates the area of eye black block.
本文主要做了以下几个方面的工作:
(1)为了提高人眼定位的准确度和速度,本文采用ASM(主动形状模型)进行人眼的粗定位,然后单独对人眼区域进行肤色分割,实现人眼细定位,细定位的结果是得到5个人眼特征点。实验表明该方法速度快,准确率高。
(2)为了达到实时性要求,定位之后就要进行人眼的跟踪,本文提出了一种新颖的眼睛跟踪方法,即综合Kalman滤波和ASM算法来进行人眼跟踪。整个跟踪过程分为两个阶段:首先根据上一帧图像中人眼的位置运用Kalman滤波预测在下一帧中人眼的位置,然后以此作为ASM的初始状态,跟踪人眼。
(3)根据跟踪到的人眼进行眼睛闭合状态识别和凝视方向计算。本文提出新颖的方法即对人眼区域的肤色分割图计算人眼黑色块面积的方法来计算人眼闭合程度;对于凝视方向,本文通过获取的5个人眼特征点以几何的方法进行计算,将凝视方向分为左中右,上中下共9个方向以进行区分,方法简单、快速、效果好。
(4)根据单一参数评判疲劳程度效果并不是很好,本文综合人眼闭合程度和凝视方向进行司机驾驶疲劳的检测,建立新的评判规则。
本文的创新点主要有以下三个方面:
(1)虽然以前也有人在疲劳检测的时候提到人眼凝视方向,但并没有真正指明如何计算人眼凝视方向,并提出评判规则。本文用图像处理的方法计算人眼凝视方向,并与PERCLOS一起建立疲劳检测的评判规则。
(2)在人眼定位与跟踪算法上,本文将ASM与基于肤色的方法相结合,速度快、效果好。
(3)相对于通过模板匹配的方法计算人眼闭合程度,本文所采用的通过计算人眼黑色块的面积的方法更加简洁明了,可靠性更高。
Nowadays, many traffic accidents take place because of the fatigued drivers. Therefore, it is significant to detect drivers' fatigue state to decrease these traffic accidents. In this thesis, fatigue detection methods have been studied and driver's fatigue level has been estimated according to his eyelid movement (eye state) and gaze direction synthetically.
In this thesis four facets work has been done.
Firstly, in order to improve the veracity and speed of eye detection, ASM has been used for coarse eye detection and then segments the eye region alone for careful eye detection, the purpose of careful eye detection is to obtain five eye feature points. The Experiments indicate that this method is fast and accurate.
Secondly, for the purpose of real time ability, this thesis proposes a novel eyes tracking method after the detecting of the eyes, which synthesizes Kalman and ASM arithmetic. The whole tracking process is separated into two steps: first of all, using Kalman to estimate the location of the eyes in the next frame according to the location in the previous frame, and then take it as the initial state of the ASM to track the eyes.
Thirdly, calculates eye state and gaze direction according to the results of eye detection. This thesis proposes a novel method to get the eye state by calculating the eye black block area in segmentation image. Gaze direction has been divided into nine parts, each part can be calculated by geometrical method according to five eye feature points, which has several advantages such as briefness、celerity、better effect.
Fourthly, using single parameter to estimate fatigue level doesn't work well, so this thesis combines eye status and gaze direction to create new formulas for driver fatigue detection.
This thesis has three innovations:
Though gaze direction was proposed before in fatigue detection, the way to calculate gaze direction didn't designate clearly. The method of image processing has been used to calculate the gaze direction and then createfatigue detection formulas with PERCLOS.
This thesis proposes a novel method to combine ASM and skin color segmentation for eye detecting and tracking, which is faster and better than others.
Compared with the method of using template matching to calculate eye status, the method this thesis used is more succinct and credible, which calculates the area of eye black block.
引文
[1]FHWA report number:FHWA-MC-97-001[R].http://fhwa.dot.gov,1997.10
[2]C.J.Reissman.The Alert Driver:A Trucker's Guide to Sleep,Fatigue,and Rest in our 24-Hour Society[J].American Trucking Associations,2200 Mill Road,Alexandria,USA 1996
[3]Knipling R.R.,Wang J.S.,Kanianthra,J.N.Current NHTSA Drowsy Driver R&D.Proceedings of the Fifteenth International Technical Conference on the Enhanced Safety of Vehicles[R].Melbourne,Australia,1996
[4]Dinges D.F.,Mallis M.M.,Maislin G.J.W.Final Report:Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and as the Basis for Alertness Management[R].National Highway Traffic Safety Administration,Report No DOT HS 808 762
[5]Carroll R.J.(Ed.).Ocular Measures of Driver Alertness[A].Technical Conference Proceedings FHWA Technical Report No.FHWA-MC-99-136[C].Washington,DC:Federal Highway Administration,Office of Motor Carrier and Highway Safety,1999
[6]W.Wierwille.Research on vehicle-based driver status/performance monitoring:development,validation,and refinement of algorithms for detection of driver drowsiness[R].Washington,DC:National Highway Traffic Safety Administration,1994
[7]郑培,宋正河,周一鸣.机动车驾驶员疲劳测评方法的研究状况及发展趋势[J].中国农业大学学报,2001,6(6):101-105
[8]周鹏.疲劳事故隐患消除技术与方法[J].汽车电器,1998(8):27-30
[9]李琪,宋凯.疲劳驾驶预警装置的实现与软件设计[J].辽宁大学学报自然科学版,2003,30(4):367-369
[10]张殿业,金键,杜文.机车司机失衡性实时监测系统[J].西南交通大学学报,1999,34(5):594-597
[11]石坚,吴远鹏,卓斌等.汽车驾驶员主动安全性因素的辨识与分析[J].上海交通大学学报,2000,34(4):441-444
[12]李增勇,王成焘.驾驶疲劳与汽车人机工程学初探[J].机械设计与制造工程,2001,30(5):12-14
[13]陈艳琴.关于司机疲劳监测的人眼检测与跟踪研究[D].长沙:中南大学,2004
[14]曹倩霞.基于眼睑运动的司机疲劳检测[D].长沙:中南大学,2005
[15]李恒峰.基于综合集成的驾驶疲劳识别[D].长沙:中南大学,2005
[16]范宏深,倪国强,申会堂.用黑斑的椭圆性质在二值化图像中探测眼睛[J].光学技术,2001,27(4):307-309
[17]刘文予,潘锋.离散对称变换在人脸图像眼睛定位中的应用[J].红外与毫米波学报,2001,20 (5):375-380
[18]解明,马泳.基于对称变换的人脸图像眼睛定位方法[J].光学技术,2004,30(2):237-239
[19]Lam Kin-Man,Yan Hong.Locating and Extracting the Eye in Human Face Image[J].Pattern Recognition,1996,29(5):771-779
[20]Kawaguchi T,Hidaka D,Rizon M.Detection of the eyes from human faces by Hough transform and separability filter[J].Proceedings of 7th IEEE International Conference on Image Processing,2000,1:49-52
[21]孙大瑞,吴乐南.基于Gabor变换的人眼定位[J].电路与系统学报,2001,6(4):29-32
[22]Cootes,Taylor,et al.Active Shape Models-Their Training and Application[J].Computer Vision and Image Understanding,1995,61(1):38-59
[23]M Kass,A Witkin and D Terzopoulos.Snakes:active contour Model[J].Proc.first international(?)conference on computer vision.IEEE Computer Society Press,1987,259-268
[24]土碧泉,陈祖荫.模式识别[M].北京:地震出版社,1989:39-51
[25]张宏林,Visual C++数字图像模式识别技术及工程实践[M].人民邮电出版社,365-366,2003
[26]Y.Ebisawa.Unconstrained pupil detection technique using tow light sources and the image difference method[J].Visualization and Intelligent Design in engineering and architecture,1995,79-80
[27]C.H.Morimoto and M.Flickner.Real-time multiple face detection using active illumination[J].Proc.Of the 4th IEEE International Conference on Automatic Face and Gesture Recognition 2000,Grenoble,France,March,2000
[28]A.Haro,M.Flickner,I.Essa.Detecting and Tracking Eyes by Using Their physiological Properties,Dynamics,and Appearance[C].Proceeding IEEE CVPR 2000,Hilton Head Island,South Carolina,June 2000
[29]Q.Ji and X.Yang,Real time visual cues extraction for monitoring driver vigilance[J].In proc.Of International Workshop on Computer Vision Systems,July 7-8,2001,Vancouver,Canada
[30]Paul Smith,Mubarak Shah and N.da Vitoria Lobo.Monitoring head/eye motion for driver alertness with one camera[J].Proceedings of the International Conference on Pattern Recognition,Session P4.3A,2000
[31]A.M.Tekalp,Digital Video Processing[C].Englewood Clifs,NJ:Prentice Hall PTR,1998
[32]S.Geman,D.Geman.Stochastic Relaxation,Gibbs Distributions,and the Bayesian Restoration of Image[J].IEEE Trans PAMI,6(6):721-741,Nov.,1984
[33]J.Konrad,E.Dubios.Bayesian Estimation of Motion Vector Fields[J].IEEE Trans PAM 14(9):910-927,Sep,1992
[34]Y.Cheng.Mean shift,Mode seeking,and Clusering[J].in IEEE Trans.PAMI,1995,17(2):790-799
[35]De la Torre,Fernando,Rubio.Subspace eye tracking for driver warning[J].IEEE International Conference on Image Processing,v3,2003
[36]D'Orazio T,Leo M,Distante A.Eye detection in face images for a driver vigilance system[J].IEEE Intelligent Vehicles Symposium,Proceedings,2004
[37]LAURENCE R,YOUNG AND DAVID SHEEN.A Survey of Eye Movement Recording Methods[J].Behavior Research Methods and Instrumentation,Vol.7,No.5,pp.397-429,1975
[38]GLENN A.MYERS,KEITH R.SHERMAN,AND LAWRENCE STARK,Eye Mornitor[J].IEEE Computer Magazine,Vol.March,pp.14-21,1991
[39]YOSHINOBU EBISAWA,Improved Video-Based Eye-Gaze Detection Method[J].IEEE IMTC '94,Hamamatsu,May,1998
[40]THOMAS E.HUTCHINSON,K.PRESTON WHITE,JR.,WORTHY N.MARTIN,KELLY C.REICHERT,AND LISA A.FREY,Human-Computer Interaction Using Eye-Gaze Input[J].IEEE Trans.on Systems,Man,and Cybernetics,Vol.19,No.6,pp.1527-1534,1998
[41]C.COLOMBO,S.ANDRONICO,AND P.DARIO.Prototype of a Vision-Based Gaze-Driven Man-Machine Interface[J].Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems,August,1995
[42]CHRISTOPHE COLLET,ALAIN FINKEL,AND RACHID GHERBI.CapRe:A Gaze Tracking System in Man-Machine Interaction[J].Proceedings of IEEE International Conference on Intelligent Engineering Systems,September,1997
[43]BAOSHEN Hu AND MINGI-IUA QIU.A New Method for Human-Computer Interaction by using Eye Gaze[J].Proceedings of IEEE International Conference on Systems,Man and Cybernetics,October,1994
[44]RAINER STIEFELHAGEN.Gaze Tracking for Multimodal Human-Computer Interaction[J].Diplomarbeit,Universitiit Karlsruhe,September,1996
[45]SHUMEET BALUJA,DEAN POMERLEAU.Non-Intrusive Gaze Tracking Using Artificial Neural Networks[R].CMU Technical Report,CMU-CS-94-102,School of Computer Science,Carnegie Mellon University,January,1994
[46]J.G.Wang,E.Sung and R.Venkateswarku.Eye Gaze Estimation from a Single Image of One Eye[J].in.Proceedings of the 9th IEEE International Conference on Computer Vision,pp.136-143, 2003
[47]郝群,刘伟华,李博.基于图像处理的人眼注视方向检测研究[J].光学技术,2004(5):547-551
[48]Tian Y L.Kanade T.Cohn J F.Recognizing upper face actions for facial expression analysis.In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR'00)[C].Hilton Head Island,South Carolina,USA,2000,1:294-301
[49]Kenji Ogawa and Mitsuo Shimotani.A Drowsiness Detection System[R].Technical Reports.March,1997
[50]Qiang Ji.RPI Fatigue Monitor Narrative[Z].http://www.ecse.rpi.edu/homepages/qji/Fatigue/fatigue_narrative.doc
[51]Dinges,D.F.,Mallis M.,Maislin,G.&Powell,J.W.(1998)Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management[J].Department of Transportation Highway Safety Publication 808 762,April 1998
[52]Federal Highway Administration.Eye-activity measures of fatigue and napping as a fatigue countermeasure[Z].Federal Highway Administration Tech Brief.http://fhwa.dot.gov.1994-04
[53]Laurence H,Nick M.Review of fatigue detection and prediction technologies[Z],http://www.nrtc.Gov.au
[54]Q.Ji and X.Yang,Real time visual cues extraction for monitoring driver vigilance[C],in Proc.of International workshop on Computer Vision Systems,Vancouver,Canada,2001
[55]Q.Ji and X.Yang,Real-Time Eye,Gaze,and Face Pose Tracking for Monitoring Driver Vigilance[J].Real-time Imaging 8,357-377(2002)
[2]C.J.Reissman.The Alert Driver:A Trucker's Guide to Sleep,Fatigue,and Rest in our 24-Hour Society[J].American Trucking Associations,2200 Mill Road,Alexandria,USA 1996
[3]Knipling R.R.,Wang J.S.,Kanianthra,J.N.Current NHTSA Drowsy Driver R&D.Proceedings of the Fifteenth International Technical Conference on the Enhanced Safety of Vehicles[R].Melbourne,Australia,1996
[4]Dinges D.F.,Mallis M.M.,Maislin G.J.W.Final Report:Evaluation of Techniques for Ocular Measurement as an Index of Fatigue and as the Basis for Alertness Management[R].National Highway Traffic Safety Administration,Report No DOT HS 808 762
[5]Carroll R.J.(Ed.).Ocular Measures of Driver Alertness[A].Technical Conference Proceedings FHWA Technical Report No.FHWA-MC-99-136[C].Washington,DC:Federal Highway Administration,Office of Motor Carrier and Highway Safety,1999
[6]W.Wierwille.Research on vehicle-based driver status/performance monitoring:development,validation,and refinement of algorithms for detection of driver drowsiness[R].Washington,DC:National Highway Traffic Safety Administration,1994
[7]郑培,宋正河,周一鸣.机动车驾驶员疲劳测评方法的研究状况及发展趋势[J].中国农业大学学报,2001,6(6):101-105
[8]周鹏.疲劳事故隐患消除技术与方法[J].汽车电器,1998(8):27-30
[9]李琪,宋凯.疲劳驾驶预警装置的实现与软件设计[J].辽宁大学学报自然科学版,2003,30(4):367-369
[10]张殿业,金键,杜文.机车司机失衡性实时监测系统[J].西南交通大学学报,1999,34(5):594-597
[11]石坚,吴远鹏,卓斌等.汽车驾驶员主动安全性因素的辨识与分析[J].上海交通大学学报,2000,34(4):441-444
[12]李增勇,王成焘.驾驶疲劳与汽车人机工程学初探[J].机械设计与制造工程,2001,30(5):12-14
[13]陈艳琴.关于司机疲劳监测的人眼检测与跟踪研究[D].长沙:中南大学,2004
[14]曹倩霞.基于眼睑运动的司机疲劳检测[D].长沙:中南大学,2005
[15]李恒峰.基于综合集成的驾驶疲劳识别[D].长沙:中南大学,2005
[16]范宏深,倪国强,申会堂.用黑斑的椭圆性质在二值化图像中探测眼睛[J].光学技术,2001,27(4):307-309
[17]刘文予,潘锋.离散对称变换在人脸图像眼睛定位中的应用[J].红外与毫米波学报,2001,20 (5):375-380
[18]解明,马泳.基于对称变换的人脸图像眼睛定位方法[J].光学技术,2004,30(2):237-239
[19]Lam Kin-Man,Yan Hong.Locating and Extracting the Eye in Human Face Image[J].Pattern Recognition,1996,29(5):771-779
[20]Kawaguchi T,Hidaka D,Rizon M.Detection of the eyes from human faces by Hough transform and separability filter[J].Proceedings of 7th IEEE International Conference on Image Processing,2000,1:49-52
[21]孙大瑞,吴乐南.基于Gabor变换的人眼定位[J].电路与系统学报,2001,6(4):29-32
[22]Cootes,Taylor,et al.Active Shape Models-Their Training and Application[J].Computer Vision and Image Understanding,1995,61(1):38-59
[23]M Kass,A Witkin and D Terzopoulos.Snakes:active contour Model[J].Proc.first international(?)conference on computer vision.IEEE Computer Society Press,1987,259-268
[24]土碧泉,陈祖荫.模式识别[M].北京:地震出版社,1989:39-51
[25]张宏林,Visual C++数字图像模式识别技术及工程实践[M].人民邮电出版社,365-366,2003
[26]Y.Ebisawa.Unconstrained pupil detection technique using tow light sources and the image difference method[J].Visualization and Intelligent Design in engineering and architecture,1995,79-80
[27]C.H.Morimoto and M.Flickner.Real-time multiple face detection using active illumination[J].Proc.Of the 4th IEEE International Conference on Automatic Face and Gesture Recognition 2000,Grenoble,France,March,2000
[28]A.Haro,M.Flickner,I.Essa.Detecting and Tracking Eyes by Using Their physiological Properties,Dynamics,and Appearance[C].Proceeding IEEE CVPR 2000,Hilton Head Island,South Carolina,June 2000
[29]Q.Ji and X.Yang,Real time visual cues extraction for monitoring driver vigilance[J].In proc.Of International Workshop on Computer Vision Systems,July 7-8,2001,Vancouver,Canada
[30]Paul Smith,Mubarak Shah and N.da Vitoria Lobo.Monitoring head/eye motion for driver alertness with one camera[J].Proceedings of the International Conference on Pattern Recognition,Session P4.3A,2000
[31]A.M.Tekalp,Digital Video Processing[C].Englewood Clifs,NJ:Prentice Hall PTR,1998
[32]S.Geman,D.Geman.Stochastic Relaxation,Gibbs Distributions,and the Bayesian Restoration of Image[J].IEEE Trans PAMI,6(6):721-741,Nov.,1984
[33]J.Konrad,E.Dubios.Bayesian Estimation of Motion Vector Fields[J].IEEE Trans PAM 14(9):910-927,Sep,1992
[34]Y.Cheng.Mean shift,Mode seeking,and Clusering[J].in IEEE Trans.PAMI,1995,17(2):790-799
[35]De la Torre,Fernando,Rubio.Subspace eye tracking for driver warning[J].IEEE International Conference on Image Processing,v3,2003
[36]D'Orazio T,Leo M,Distante A.Eye detection in face images for a driver vigilance system[J].IEEE Intelligent Vehicles Symposium,Proceedings,2004
[37]LAURENCE R,YOUNG AND DAVID SHEEN.A Survey of Eye Movement Recording Methods[J].Behavior Research Methods and Instrumentation,Vol.7,No.5,pp.397-429,1975
[38]GLENN A.MYERS,KEITH R.SHERMAN,AND LAWRENCE STARK,Eye Mornitor[J].IEEE Computer Magazine,Vol.March,pp.14-21,1991
[39]YOSHINOBU EBISAWA,Improved Video-Based Eye-Gaze Detection Method[J].IEEE IMTC '94,Hamamatsu,May,1998
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