基于反向双目识别的驾驶员分心检测
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摘要
为检测分心驾驶状态,研究了基于反向双目的驾驶状态检测方法。首先,根据Hough算法进行车道线检测和识别,计算车辆偏航率;同时采用多点透视算法对驾驶员头部姿态进行估计;然后建立基于高斯隶属度函数模糊判断规则,根据车辆偏航率与驾驶员头部姿态对驾驶员驾驶状态进行识别。最后,采用所建立的驾驶员驾驶状态识别模型,对车道保持、换道行驶及分心行驶三种不同驾驶状态进行测试。结果表明,建立的驾驶员驾驶状态识别模型对上述三种状态检测准确率分别为99.0%、86.7%、80.8%。
In order to detect the driver distraction,the detection method of driver behavior was proposed based on reverse binocular recognition. Firstly,vehicle departure rate was obtained by lane detection and recognition based on Hough transformation. At the same time,perspective-n-point( PNP) algorithm was used to estimate the head pose of driver. Then,a fuzzy rule based on Gaussian membership function was established,with which the driver behavior was obtained base on the head pose and vehicle departure rate. Finally,the established driver behavior recognition model was used to test three different driving states including lane keeping,lane changing and distraction driving. The results show that accuracy rates of the driver behavior recognition for the above three states are 99. 0%,86. 7%,80. 8% respectively.
In order to detect the driver distraction,the detection method of driver behavior was proposed based on reverse binocular recognition. Firstly,vehicle departure rate was obtained by lane detection and recognition based on Hough transformation. At the same time,perspective-n-point( PNP) algorithm was used to estimate the head pose of driver. Then,a fuzzy rule based on Gaussian membership function was established,with which the driver behavior was obtained base on the head pose and vehicle departure rate. Finally,the established driver behavior recognition model was used to test three different driving states including lane keeping,lane changing and distraction driving. The results show that accuracy rates of the driver behavior recognition for the above three states are 99. 0%,86. 7%,80. 8% respectively.
引文
1 Klauer S G,Dingus T A,Neale T V,et al.The impact of driver inattention on near-crash/crash risk:An analysis using the 100-Car naturalistic driving study data.Washington,D.C.:NHTSA,2006
2 Lee J D,Young K L,Regan M A.Defining driver distraction:theory,effects,and mitigation.Boca Raton:CRC Press,2008;13(4):31-40
3 Victor T W,Harbluk J L,Engstr9m J A.Sensitivity of eye-movement measures to in-vehicle task difficulty.Transportation Research Part F:Traffic Psychology and Behaviour,2005;8(2):167-190
4 Recarte M A,Nunes L M.Effects of verbal and spatial-imagery tasks on eye fixations while driving.Journal of Experimental Psychology:Applied,2000;6(1):31-43
5 Recarte M A,Nunes L M.Mental workload while driving:effects on visual search,discrimination,and decision making.Journal of Experimental Psychology:Applied,2003;9(2):119-137
6 Backs R W,Lenneman J K,Wetzel J M,et al.Cardiac measures of driver workload during simulated driving with and without visual occlusion.Human Factors,2003;45(4):525-538
7 Van d H R.Occlusion as a measure for visual workload:an overview of TNO occlusion research in car driving.Applied Ergonomics,2004;35(3):189-196
8 Harms L,Patten C.Peripheral detection as a measure of driver distraction:A study of memory-based versus system-based navigation in a built-up area.Transportation Research Part F:Traffic Psychology and Behaviour,2003;6(1):23-36
9 Patten C J D,Kircher A,Ostlund J,et al.Using mobile telephones:cognitive workload and attention resource allocation.Accident Analysis&Prevention,2004;36(3):341-350
10 Doshi A,Trivedi M.Investigating the relationships between gaze patterns,dynamic vehicle surround analysis,and driver intentions.Intelligent Vehicles Symposium.New York:IEEE,2009:887-892
11 Liang Y,Reyes M L,Lee J D.Real-time detection of driver cognitive distraction using support vector machines.IEEE Transactions on Intelligent Transportation Systems,2007;8(2):340-350
12马勇,石涌泉,付锐,等.驾驶人分心时长对车道偏离影响的实车试验.吉林大学学报(工学版),2015;(4):1095-1101Ma Yong,Shi Yongquan,Fu Rui,et al.Impact of driver's distracted driving time on vehicle lane departure.Journal of Jilin University(Engineering and Technology Edition),2015;(4):1095-1101
13余厚云,张为公.直线模型下的车道线跟踪与车道偏离检测.自动化仪表,2009;(11):1-3Yu Houyun,Zhang Weigong.Lane tracking and departure detection based on linear model.Process Automation Instrumentation,2009;(11):1-3
14孔英会,高超,车辚辚.一种基于显著性和部件模型的无约束条件人脸检测方法.科学技术与工程,2016;16(34):97-102Kong Yinghui,Gao Chao,Che Linlin.A method using saliency and part-based model for automatic face detection in unconstrained conditions.Science Technology and Engineering,2016;16(34):97-102
15陈书明,陈锻生.基于改进LPP的头部姿态估计方法.计算机工程与设计,2011;32(12):4218-4222Chen Shuming,Chen Duansheng.Head pose estimation based on improved LPP.Computer Engineering and Design,2011;32(12):4218-4222
16李利民,刘明辉.基于机器学习算法的人脸识别鲁棒性研究.中国电子科学研究院学报,2017;12(2):219-224Li limin,Liu Minghui.Research on the robustness of face recognition based on machine learning algorithms.Journal of China Academy of Electronics and Information Technology,2017;12(2):219-224
17周彩霞,易江义.基于改进BP网络的人脸检测与定位.科学技术与工程,2008;8(6):1605-1609Zhou Caixia,Yi Jiangyi.Human face detection and location method based on improved BP network.Science Technology and Engineering,2008;8(6):1605-1609
18 Zhou M,Liang L,Sun J,et al.AAM based face tracking with temporal matching and face segmentation.Computer Vision and Pattern Recognition.New York:IEEE,2010:701-708
19秦丽娟,胡玉兰,魏英姿,等.基于模型的单目视觉定位方法研究概述.第三届全国虚拟仪器大会论文集.桂林:仪器仪表学报,2008:530-533Qin Lijuan,Hu Yulan,Wei Yingzi,et al.Research overview of location method for model-based monocular vision.The 3rd National Virtual Instruments Conference.Guilin:Chinese Joumal of Scientmc Instrument,2008:530-533
2 Lee J D,Young K L,Regan M A.Defining driver distraction:theory,effects,and mitigation.Boca Raton:CRC Press,2008;13(4):31-40
3 Victor T W,Harbluk J L,Engstr9m J A.Sensitivity of eye-movement measures to in-vehicle task difficulty.Transportation Research Part F:Traffic Psychology and Behaviour,2005;8(2):167-190
4 Recarte M A,Nunes L M.Effects of verbal and spatial-imagery tasks on eye fixations while driving.Journal of Experimental Psychology:Applied,2000;6(1):31-43
5 Recarte M A,Nunes L M.Mental workload while driving:effects on visual search,discrimination,and decision making.Journal of Experimental Psychology:Applied,2003;9(2):119-137
6 Backs R W,Lenneman J K,Wetzel J M,et al.Cardiac measures of driver workload during simulated driving with and without visual occlusion.Human Factors,2003;45(4):525-538
7 Van d H R.Occlusion as a measure for visual workload:an overview of TNO occlusion research in car driving.Applied Ergonomics,2004;35(3):189-196
8 Harms L,Patten C.Peripheral detection as a measure of driver distraction:A study of memory-based versus system-based navigation in a built-up area.Transportation Research Part F:Traffic Psychology and Behaviour,2003;6(1):23-36
9 Patten C J D,Kircher A,Ostlund J,et al.Using mobile telephones:cognitive workload and attention resource allocation.Accident Analysis&Prevention,2004;36(3):341-350
10 Doshi A,Trivedi M.Investigating the relationships between gaze patterns,dynamic vehicle surround analysis,and driver intentions.Intelligent Vehicles Symposium.New York:IEEE,2009:887-892
11 Liang Y,Reyes M L,Lee J D.Real-time detection of driver cognitive distraction using support vector machines.IEEE Transactions on Intelligent Transportation Systems,2007;8(2):340-350
12马勇,石涌泉,付锐,等.驾驶人分心时长对车道偏离影响的实车试验.吉林大学学报(工学版),2015;(4):1095-1101Ma Yong,Shi Yongquan,Fu Rui,et al.Impact of driver's distracted driving time on vehicle lane departure.Journal of Jilin University(Engineering and Technology Edition),2015;(4):1095-1101
13余厚云,张为公.直线模型下的车道线跟踪与车道偏离检测.自动化仪表,2009;(11):1-3Yu Houyun,Zhang Weigong.Lane tracking and departure detection based on linear model.Process Automation Instrumentation,2009;(11):1-3
14孔英会,高超,车辚辚.一种基于显著性和部件模型的无约束条件人脸检测方法.科学技术与工程,2016;16(34):97-102Kong Yinghui,Gao Chao,Che Linlin.A method using saliency and part-based model for automatic face detection in unconstrained conditions.Science Technology and Engineering,2016;16(34):97-102
15陈书明,陈锻生.基于改进LPP的头部姿态估计方法.计算机工程与设计,2011;32(12):4218-4222Chen Shuming,Chen Duansheng.Head pose estimation based on improved LPP.Computer Engineering and Design,2011;32(12):4218-4222
16李利民,刘明辉.基于机器学习算法的人脸识别鲁棒性研究.中国电子科学研究院学报,2017;12(2):219-224Li limin,Liu Minghui.Research on the robustness of face recognition based on machine learning algorithms.Journal of China Academy of Electronics and Information Technology,2017;12(2):219-224
17周彩霞,易江义.基于改进BP网络的人脸检测与定位.科学技术与工程,2008;8(6):1605-1609Zhou Caixia,Yi Jiangyi.Human face detection and location method based on improved BP network.Science Technology and Engineering,2008;8(6):1605-1609
18 Zhou M,Liang L,Sun J,et al.AAM based face tracking with temporal matching and face segmentation.Computer Vision and Pattern Recognition.New York:IEEE,2010:701-708
19秦丽娟,胡玉兰,魏英姿,等.基于模型的单目视觉定位方法研究概述.第三届全国虚拟仪器大会论文集.桂林:仪器仪表学报,2008:530-533Qin Lijuan,Hu Yulan,Wei Yingzi,et al.Research overview of location method for model-based monocular vision.The 3rd National Virtual Instruments Conference.Guilin:Chinese Joumal of Scientmc Instrument,2008:530-533