基于图像处理的疲劳驾驶预警研究
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摘要
基于图像处理知识对疲劳驾驶检测系统进行研究。在构建人脸相关数据库后,主要的疲劳相关信息通过混合模型算法进行获取。首先对图片进行一定的预处理,增加图像增强模块消除实际的光照干扰。采用基于AdaBoost的人脸检测算法为核心检测方法,对人脸的定位以驾驶员眼部特征为关键部位。采用人眼变化曲线分析法与PERCLOS准则相结合的判定方法进行疲劳判定。针对不同需要和特性对疲劳驾驶预警进行优化。实验通过模拟测试验证了关键点定位算法的可实现性与准确性,并验证实际的疲劳测试具有较好的可靠性。
This paper researches the fatigue driving detection system based on the knowledge of image processing. After constructing the face correlation database, the main fatigue related information is obtained by the hybrid model algorithm. Firstly, the image is preprocessed, and the image enhancement module is added to eliminate the actual illumination interference. The face detection algo-rithm based on AdaBoost is used as the core detection method, and the positioning of the face is based on the driver ′ s eye feature. Fatigue determination is performed using a combination of human eye curve analysis and PERCLOS criteria. Fatigue driving warnings are optimized for different needs and characteristics. The experiment verifies the achievability and accuracy of the key point location algorithm through simulation test, and verifies that the actual fatigue test has good reliability.
This paper researches the fatigue driving detection system based on the knowledge of image processing. After constructing the face correlation database, the main fatigue related information is obtained by the hybrid model algorithm. Firstly, the image is preprocessed, and the image enhancement module is added to eliminate the actual illumination interference. The face detection algo-rithm based on AdaBoost is used as the core detection method, and the positioning of the face is based on the driver ′ s eye feature. Fatigue determination is performed using a combination of human eye curve analysis and PERCLOS criteria. Fatigue driving warnings are optimized for different needs and characteristics. The experiment verifies the achievability and accuracy of the key point location algorithm through simulation test, and verifies that the actual fatigue test has good reliability.
引文
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[16]连佳佳,王志有,王利斌,等.基于JPEG2000算法的图像编码装置的设计[J].电子技术应用,2019,45(3):46-49.
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[19]WANG H,ZHOU L B,YING Y.A novel approach for real time eye state detection in fatigue awareness system[C].Proceedings of International Conference on Robotics Automation and Mechantronics.New YORK,USA:ACMPress,2010:528-532.
[20]姚胜,李晓华,张卫华,等.基于LBP的眼睛开闭检测方法[J].计算机应用研究,2015,32(6):1897-1901.
[21]杨晋吉,李荣兵.基于Adaboost与Clifford代数的人脸检测[J].计算机工程,2013,9(9):214-217.
[22]旷文腾,毛宽诚,黄家才,等.基于高斯眼白模型的疲劳驾驶检测[J].中国图象图形学报,2016,21(11):1515-1522.
[2]NOBE S A,WAG F Y.An overview of recent develop ments in automated lateral and longitudinal vehicle controls[C].Proceedings of IEEE International Conference on Systems,Man,and Cybernetics.Washington D.C.,USA:IEEE Press,2001:3447-3452.
[3]刘在英,朱琳玲,杨平.基于肤色模型和中线定位的人脸检测算法[J].计算机工程,2013,39(1):200-203.
[4]刘春生,常发亮,陈振学,等.改进的高斯肤色模型及其在人脸检测中的应用[J].仪器仪表学报,2012,33(5):1117-1121.
[5]LEE B G,JUNG S J,CHUNG W Y.Realtime physiological and vision monitoring of vehicle driver for non-in trusive drowsiness detection[J].IET Communications,2011,5(17):2461-2469.
[6]OMIDYEGANEH M,JAVADTALAB A,SHIMOHAMMADI SIntelligent driver drowsiness detection through fusion of yawning and eye closure[C].Proceedings of IEEE International Conference on Virtual Environments Human Computer Interfaces&Measurement Systems.Ottawa,Canada:IEEEPress,2011:18-23.
[7]赵文,张意,张卫华,等.基于红外图像的眼睛开闭检测方法[J].计算机工程与设计,2015,36(2):436-440.
[8]KNIPLING R R,WIERWILLE W W.Vehicle-based drowsy driver detection:current status and future prospects[C].Proceedings of IVHS′94.Atlanta,USA,1994:1-24.
[9]LECUN Y,BOSER B,DENKER J S,et al.Back propagation applied to handwritten zip code recognition[J].Neural Computation,1989,1(4):541-551.
[10]HENRIQUES J F,CASEIROR,MARTINS P,et al.Highspeed tracking with kernelized correlation filters[J].IEEETransactions on Pattern Analysis&Machine Intelligence,2015(3):583-596.
[11]HENRIQUES J F,RUI C,MARTINS P,et al.Exploiting the circulent structure of tracking-by-detection with kernels[C].Proceedings of ECCV′12.Roma,Italy,2012:702-715.
[12]邓健康.基于级联回归模型的人脸配准研究[D].南京:南京信息工程大学,2015.
[13]吴宇豪,安籽鹏.面向图像三维重建的无人机航线规划[J]电子技术应用,2019,45(3):76-79,87.
[14]CUN Y L,HAN C,WANG C,et al.The application of a convolution neural network on face and license plate detection[C].Proceedings of the 18th International Conference on Pattern Recognition.Washington D.C,USA:IEEEPress,2006:552-555.
[15]ZEILE M D,FERGUS R.Stochastic pooling for regularization of deep convolutional neural networks[J].Computer Science,2012,7575(1):702-715.
[16]连佳佳,王志有,王利斌,等.基于JPEG2000算法的图像编码装置的设计[J].电子技术应用,2019,45(3):46-49.
[17]吴康华.基于PERCLOS的驾驶疲劳检测系统设计[D].杭州:浙江大学,2008.
[18]JAVIE F M.Driver drowsiness warning system using visual information for both diurnal and nocturnal illumination conditions[J].EUASIP Journal on Advances in Signal Processing,2010(1):1-19.
[19]WANG H,ZHOU L B,YING Y.A novel approach for real time eye state detection in fatigue awareness system[C].Proceedings of International Conference on Robotics Automation and Mechantronics.New YORK,USA:ACMPress,2010:528-532.
[20]姚胜,李晓华,张卫华,等.基于LBP的眼睛开闭检测方法[J].计算机应用研究,2015,32(6):1897-1901.
[21]杨晋吉,李荣兵.基于Adaboost与Clifford代数的人脸检测[J].计算机工程,2013,9(9):214-217.
[22]旷文腾,毛宽诚,黄家才,等.基于高斯眼白模型的疲劳驾驶检测[J].中国图象图形学报,2016,21(11):1515-1522.