基于嵌入式的人眼信息检测系统研究
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摘要
根据统计分析,疲劳驾驶是引发交通事故的主要原因之一,因此及时有效地检测出驾驶员的疲劳状态,以减少此类交通事故的发生,有着重要的现实意义。本文通过CCD摄像头实时采集驾驶员的头部图像,设计了一种基于TMS320DM642 DSP芯片的人眼信息检测系统,用于检测驾驶员的疲劳状态。
本文重点对检测系统中的人眼定位、人眼跟踪和人眼状态识别模块及其算法在DSP中的实现作了研究。首先提出了一种基于blob分析的肤色人脸检测方法,这种算法能够有效去除彩色图像中的类肤色噪声;接着进行人眼定位,采用了基于灰度投影法和改进型Hough变换法实现人眼的精确定位;在跟踪算法上,采用了虹膜跟踪和基于Kalman预测的模板匹配法相结合的方法实现眼睛的实时跟踪;在接下来的人眼状态识别中,提出了基于Gabor变换提取眼睛特征和支持向量机相结合的方法;最后综合PERCLOS和AECS参数对驾驶员的疲劳度进行判别。
在理论研究的基础之上,基于SEED-DM642开发平台,在CCS环境下采用C语言编程实现人眼信息检测系统并且调试完成。实验结果表明,该系统能够快速、准确地定位人眼并且识别人眼状态,从而有效地判定出驾驶员的疲劳状态。
According to statistical analysis,fatigue driving is one of the main reasons that caused traffic accidents, so detecting driver fatigue timely and effectively and reduce the traffic accident caused by driver fatigue has important practical significance. Real-time collecting driver’s images through CCD camera, this paper designs a detection system of eye information based on the TMS320DM642 DSP chip. The system is used to detect driver fatigue.
This paper mainly research on the eye location, eye tracking and eye state recognition in detection system and theirs algorithm implementation in the DSP. First skin color face detection algorithm based on blob analysis was proposed, which can remove the background noise whose color is similar to human skin effectively; Then to locate eye, locating eyes accurately using gray projection algorithm and modified Hough transform; In the eye tracking, using iris tracking and the template matching algorithm based on Kalman prediction to tracking eyes real-time; In the eye state recognition, the method of combining Gabor transform extracting eye characteristic and SVM was proposed to recognizing eye state, open or closed; finally using PERCLOS and AECS parameters to detect driver fatigue.
This paper use C language to program and debug in the CCS environment based on the theoretical study and SEED-DM642 system development platform. The experiment results show that the system can locate the eye and recognize eye state quickly and accurately, so it can detect drowsiness effectively.
本文重点对检测系统中的人眼定位、人眼跟踪和人眼状态识别模块及其算法在DSP中的实现作了研究。首先提出了一种基于blob分析的肤色人脸检测方法,这种算法能够有效去除彩色图像中的类肤色噪声;接着进行人眼定位,采用了基于灰度投影法和改进型Hough变换法实现人眼的精确定位;在跟踪算法上,采用了虹膜跟踪和基于Kalman预测的模板匹配法相结合的方法实现眼睛的实时跟踪;在接下来的人眼状态识别中,提出了基于Gabor变换提取眼睛特征和支持向量机相结合的方法;最后综合PERCLOS和AECS参数对驾驶员的疲劳度进行判别。
在理论研究的基础之上,基于SEED-DM642开发平台,在CCS环境下采用C语言编程实现人眼信息检测系统并且调试完成。实验结果表明,该系统能够快速、准确地定位人眼并且识别人眼状态,从而有效地判定出驾驶员的疲劳状态。
According to statistical analysis,fatigue driving is one of the main reasons that caused traffic accidents, so detecting driver fatigue timely and effectively and reduce the traffic accident caused by driver fatigue has important practical significance. Real-time collecting driver’s images through CCD camera, this paper designs a detection system of eye information based on the TMS320DM642 DSP chip. The system is used to detect driver fatigue.
This paper mainly research on the eye location, eye tracking and eye state recognition in detection system and theirs algorithm implementation in the DSP. First skin color face detection algorithm based on blob analysis was proposed, which can remove the background noise whose color is similar to human skin effectively; Then to locate eye, locating eyes accurately using gray projection algorithm and modified Hough transform; In the eye tracking, using iris tracking and the template matching algorithm based on Kalman prediction to tracking eyes real-time; In the eye state recognition, the method of combining Gabor transform extracting eye characteristic and SVM was proposed to recognizing eye state, open or closed; finally using PERCLOS and AECS parameters to detect driver fatigue.
This paper use C language to program and debug in the CCS environment based on the theoretical study and SEED-DM642 system development platform. The experiment results show that the system can locate the eye and recognize eye state quickly and accurately, so it can detect drowsiness effectively.
引文
[1] Transport Research Laboratory.Driver fatigue-a killer on the roads. http://www.ntsb.gov/itsal_private.htlm
[2] 2008年全国道路交通事故情况. http://www.mps.gov.cn/n16/n1282/n3553/1770249.html
[3] 2009年全国道路交通事故情况. http://www.mps.gov.cn/n16/n1252/n1837/n2557/2276407.html
[4] 2010年上半年全国交通事故情况. http://www.mps.gov.cn/n16/n85753/n85870/2475028.html
[5]曹倩霞.基于眼睑运动的司机疲劳检测.中南大学硕士学位论文.2005.16-21
[6] S Boveris.Driver fatigue monitoring technologies and future ideas.Proc.AWAKE Road Safety Workshop,2004
[7]郑培,周一鸣.机动车驾驶员驾驶疲劳评价方法的研究现状及发展趋势[J].中国农业大学学报,2001.(6):53-55
[8]李峰,曾超,徐向东.驾驶防瞌睡装置中人眼快速定位方法研究[J].光学仪器,2002.(4):70-72
[9]蒋永刚.数字图像模式识别工程软件设计.北京:中国水利水电出版社,2008.19-93
[10]朱虹等编著.数字图像处理基础.科学出版社,2005.86-100,105-141
[11]章毓晋.图像工程(上册)图像处理与分析.北京:清华大学出版社,1991.12-67
[12]章毓晋.图像工程(下册)图像处理与分析.北京:清华大学出版社,1999.34-87
[13]田浩,葛秀慧,王顶.数字图像处理:原理与应用.北京:清华大学出版社,2007.13-50
[14]梁路宏,艾海舟,徐光,张钹.人脸检测研究综述[J].计算机学报.2002.(25):449-456
[15] Hsu RL, Abdel-MottalebM, Jain AK (2002) Face detection in color images. IEEE Trans Pattern Anal Mach Intell 2002.(5):696–706
[16]于威威,腾晓龙,刘重庆.复杂背景下人眼定位及人脸检测[J].计算机仿真.2004.185-187
[17]王诚.基于DSP/BIOS的低照度驾驶员疲劳检测系统的研究.西安理工大学硕士学位论文.2007.8-20
[18] Michal Kawulok. Energy-based blob analysis for improving precision of skin segmentation. Multimed Tools Appl,2010 (49):463–481
[19] Luis M Bergasa, Miguel A Sotelo.Real-Time System for Monitoring Drive Vigilance. IEEE Trans Actions On Intelligent Transportation Systems,2006(7):63-77
[20]李玉山.数字视觉视频技术.西安电子科技大学出版社,2006.96-109
[21]黎洪松.数字视频处理.北京邮电大学出版社,2006.48-107
[22] Q Ji, X Yang.Real-Time Eye,Gaze,and Face Pose Tracking for Monitoring Driver Vigilance[J].Real-Time Imaging,2002.8:357-377
[23]董文会.自然光照条件下基于计算视觉的驾驶员疲劳检测研究.山东大学硕士学位论文.2006
[24]吴康华.基于PERCLOS的驾驶疲劳检测系统设计.浙江大学硕士学位论文.2008
[25] Marco Javier Flores,JoséMaría Armingol and Arturo de la Escalera. Real-Time Warning System for Driver Drowsiness Detection Using Visual Information. Journal of Intelligent and Robotic Systems,2009
[26]田欣.基于不同颜色空间的肤色模型.西安科技学院学报,2001.(4):369-371
[27]江水郎.基于视觉的驾驶员疲劳检测研究.上海交通大学硕士学位论文.2008
[28] M Dobes,J Martinek.Human eye locatization using the modified Hough transform.Optik,2006.(117):468-473
[29]李志春.驾驶员疲劳状态检测技术研究.江苏大学博士学位论文.2009
[30] O V KOMOGORTSEV,J I KHAN.Eye movement prediction by oculomotor plantKalman filter with brainstem control.J Control theory Appl,2009.(7):14-22
[31] M. Imran Khan and A. Bin Mansoor. Real Time Eyes Tracking and Classification for Driver Fatigue Detection. Image Analysis and Recognition,2008.(5112): 729-738
[32] D W Hansen,R I Hammoud.An improved likelihood model for eye tracking.Comoputer Vision and Image Understanding 2007,106:220-230
[33]成波,张广渊,冯睿嘉等.基于眼睛状态识别的驾驶员疲劳实时监测[J].汽车工程.2008.(30):1001-1005
[34] Andrew Duchowski. Eye Tracking Techniques. Eye Tracking Methodology,2007.51-59
[35] TMS320C6000 DSP/BIOS 5.32 Application Programming Interface(API)Reference Guide.TI.SPRU403O.2007
[36]韩非,胡春海,李伟编著.TMS320C6000系列DSP开发应用技巧:重点与难点剖析.北京:中国电力出版社,2008.34-54
[37]王跃宗,刘京会编著. TMS320DM642 DSP应用系统设计与开发.北京:人民邮电出版社,2009.34-56
[38] Texas Instruments Incorporated.TMS320C6000系列DSP编程工具与指南.北京:清华大学出版社,2007
[39]汪安民,程昱,徐保根.DSP嵌入式系统开发典型案例.人民邮电出版社,2007.34-56
[40]韩相军,梁艳荣,关永.基于DSP的嵌入式驾驶疲劳监测系统研究[J].公路交通科技.2007.(24):147-150
[2] 2008年全国道路交通事故情况. http://www.mps.gov.cn/n16/n1282/n3553/1770249.html
[3] 2009年全国道路交通事故情况. http://www.mps.gov.cn/n16/n1252/n1837/n2557/2276407.html
[4] 2010年上半年全国交通事故情况. http://www.mps.gov.cn/n16/n85753/n85870/2475028.html
[5]曹倩霞.基于眼睑运动的司机疲劳检测.中南大学硕士学位论文.2005.16-21
[6] S Boveris.Driver fatigue monitoring technologies and future ideas.Proc.AWAKE Road Safety Workshop,2004
[7]郑培,周一鸣.机动车驾驶员驾驶疲劳评价方法的研究现状及发展趋势[J].中国农业大学学报,2001.(6):53-55
[8]李峰,曾超,徐向东.驾驶防瞌睡装置中人眼快速定位方法研究[J].光学仪器,2002.(4):70-72
[9]蒋永刚.数字图像模式识别工程软件设计.北京:中国水利水电出版社,2008.19-93
[10]朱虹等编著.数字图像处理基础.科学出版社,2005.86-100,105-141
[11]章毓晋.图像工程(上册)图像处理与分析.北京:清华大学出版社,1991.12-67
[12]章毓晋.图像工程(下册)图像处理与分析.北京:清华大学出版社,1999.34-87
[13]田浩,葛秀慧,王顶.数字图像处理:原理与应用.北京:清华大学出版社,2007.13-50
[14]梁路宏,艾海舟,徐光,张钹.人脸检测研究综述[J].计算机学报.2002.(25):449-456
[15] Hsu RL, Abdel-MottalebM, Jain AK (2002) Face detection in color images. IEEE Trans Pattern Anal Mach Intell 2002.(5):696–706
[16]于威威,腾晓龙,刘重庆.复杂背景下人眼定位及人脸检测[J].计算机仿真.2004.185-187
[17]王诚.基于DSP/BIOS的低照度驾驶员疲劳检测系统的研究.西安理工大学硕士学位论文.2007.8-20
[18] Michal Kawulok. Energy-based blob analysis for improving precision of skin segmentation. Multimed Tools Appl,2010 (49):463–481
[19] Luis M Bergasa, Miguel A Sotelo.Real-Time System for Monitoring Drive Vigilance. IEEE Trans Actions On Intelligent Transportation Systems,2006(7):63-77
[20]李玉山.数字视觉视频技术.西安电子科技大学出版社,2006.96-109
[21]黎洪松.数字视频处理.北京邮电大学出版社,2006.48-107
[22] Q Ji, X Yang.Real-Time Eye,Gaze,and Face Pose Tracking for Monitoring Driver Vigilance[J].Real-Time Imaging,2002.8:357-377
[23]董文会.自然光照条件下基于计算视觉的驾驶员疲劳检测研究.山东大学硕士学位论文.2006
[24]吴康华.基于PERCLOS的驾驶疲劳检测系统设计.浙江大学硕士学位论文.2008
[25] Marco Javier Flores,JoséMaría Armingol and Arturo de la Escalera. Real-Time Warning System for Driver Drowsiness Detection Using Visual Information. Journal of Intelligent and Robotic Systems,2009
[26]田欣.基于不同颜色空间的肤色模型.西安科技学院学报,2001.(4):369-371
[27]江水郎.基于视觉的驾驶员疲劳检测研究.上海交通大学硕士学位论文.2008
[28] M Dobes,J Martinek.Human eye locatization using the modified Hough transform.Optik,2006.(117):468-473
[29]李志春.驾驶员疲劳状态检测技术研究.江苏大学博士学位论文.2009
[30] O V KOMOGORTSEV,J I KHAN.Eye movement prediction by oculomotor plantKalman filter with brainstem control.J Control theory Appl,2009.(7):14-22
[31] M. Imran Khan and A. Bin Mansoor. Real Time Eyes Tracking and Classification for Driver Fatigue Detection. Image Analysis and Recognition,2008.(5112): 729-738
[32] D W Hansen,R I Hammoud.An improved likelihood model for eye tracking.Comoputer Vision and Image Understanding 2007,106:220-230
[33]成波,张广渊,冯睿嘉等.基于眼睛状态识别的驾驶员疲劳实时监测[J].汽车工程.2008.(30):1001-1005
[34] Andrew Duchowski. Eye Tracking Techniques. Eye Tracking Methodology,2007.51-59
[35] TMS320C6000 DSP/BIOS 5.32 Application Programming Interface(API)Reference Guide.TI.SPRU403O.2007
[36]韩非,胡春海,李伟编著.TMS320C6000系列DSP开发应用技巧:重点与难点剖析.北京:中国电力出版社,2008.34-54
[37]王跃宗,刘京会编著. TMS320DM642 DSP应用系统设计与开发.北京:人民邮电出版社,2009.34-56
[38] Texas Instruments Incorporated.TMS320C6000系列DSP编程工具与指南.北京:清华大学出版社,2007
[39]汪安民,程昱,徐保根.DSP嵌入式系统开发典型案例.人民邮电出版社,2007.34-56
[40]韩相军,梁艳荣,关永.基于DSP的嵌入式驾驶疲劳监测系统研究[J].公路交通科技.2007.(24):147-150