基于改进ASM算法的列车司机人眼状态检测
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摘要
为解决列车司机疲劳状态检测问题,提出了一种改进的主动形状模型(ASM)算法,并设计了一种基于支持向量机(SVM)的人眼状态判定机制,实现了对于司机驾驶过程中的人眼状态进行有效地检测。改进的ASM算法是通过利用多尺度二值模型(MB-LBP)算子来提取局部特征向量,提高了算法在复杂光线环境下的鲁棒性。并将第一步提取出来的眼睛比例作为SVM分类器的输入,用于人眼状态的检测分类。结果表明:改进的ASM算法有效地提高了人脸关键点定位的准确性,设计的判定机制在使用真实数据样本集的测试中达到了80%以上的准确度。
To solve the problem of train driver's fatigue state detection,an improved active shape model( ASM)algorithm is proposed and a fatigue judgment mechanism based on support vector machine( SVM) is designed,realizing the effective detection of the eyes state of train driver during driving. The improved ASM algorithm extracts the local eigenvectors through the multi-scale local binary pattern( MB-LBP),improving the robustness in complex light environment. Considering the effect of the time parameters for the judgment of the fatigue state of the driver,the judgment mechanism use two-level SVM structure. As the result shows,the improved ASM algorithm effectively improves the accuracy of the key point location of face,and the judgment mechanism designed in this article achieves 80 % accuracy in the real dataset testing.
To solve the problem of train driver's fatigue state detection,an improved active shape model( ASM)algorithm is proposed and a fatigue judgment mechanism based on support vector machine( SVM) is designed,realizing the effective detection of the eyes state of train driver during driving. The improved ASM algorithm extracts the local eigenvectors through the multi-scale local binary pattern( MB-LBP),improving the robustness in complex light environment. Considering the effect of the time parameters for the judgment of the fatigue state of the driver,the judgment mechanism use two-level SVM structure. As the result shows,the improved ASM algorithm effectively improves the accuracy of the key point location of face,and the judgment mechanism designed in this article achieves 80 % accuracy in the real dataset testing.
引文
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[4]黄皓.基于驾驶操作及车辆状态的疲劳驾驶行为检测研究[D].南京:东南大学,2016.
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[6]于兴玲,王民,张立材.驾驶员眼睛疲劳状态检测技术研究[J].传感器与微系统,2007,26(7):16-17,20.
[7]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(6):1897-1901.
[8]李杰.可见光/近红外人脸识别方法的研究与实现[D].北京:北京交通大学,2016.
[9]陈宇波,陈新林,陈守明.一种在红外图像中定位人眼的方法[J].传感器与微系统,2010,29(4):62-66.
[10] VAN GINNEKEN B,FRANGI A F,STAAL J J,et al. Active shape model segmentation with optimal features[J]. IEEE Transactions on Medical Imaging,2002,21(8):924-933.
[11] ZHAO Y,JIA W,HU R X,et al. Completed robust local binary pattern for texture classification[J]. Neurocomputing,2013,106:68-76.
[12] LIAO S,ZHU X,LEI Z,et al. Learning multi-scale block local binary patterns for face recognition[C]∥2007 Proceedings of the International Conference on Biometrics,Springer,2007.
[13] WANG J,ZHENG J,ZHANG S,et al. A face recognition system based on local binary patterns and support vector machine for home security service robot[C]∥Proceedings of 2016 the 9th Internation al Symposium on Computational Intelligence and Design(ISCID),IEEE,2016.
[14] GAO W,CAO B,SHAN S,et al. The CAS-PEAL large-scale Chinese face database and baseline evaluations[J]. IEEE Transactions on Systems,Man,and Cybernetics—Part A:Systems and Humans,2008,38(1):149-161.