基于多类别特征融合的疲劳检测系统研究
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摘要
针对疲劳检测中单一或同类特征指标易受干扰的问题,根据回归分析与模糊评价理论,提出一种基于多类别特征信息融合的疲劳检测系统。根据各疲劳特征的特点对其特征参数进行描述与提取,结合PVT测试完成对疲劳度的量化分级,利用回归分析在解释多变量影响强度上与模糊数学在处理非确定性问题上的优势,完成检测系统的设计与建模,并针对图像特征提取中的干扰因素提出一种优化算法。仿真实验结果表明,该系统可有效检测出驾驶员的疲劳状态,优化算法对系统性能提升明显。
The single or similar feature index in fatigue detection is easily disturbed,so a fatigue detection system based on multi-class feature information fusion is proposed according to the regression analysis and fuzzy evaluation theory. In accordance with the features of each fatigue characteristic,the feature parameters are described and extracted. The quantitative classification of fatigue degree is performed in combination with PVT test. The advantages of regression analysis for explaining the multivariate influence intensity and fuzzy mathematics for dealing with the uncertain problems are used to complete the design and modeling of the detection system. An optimization algorithm is proposed to overcome the interference factors in image feature extraction.The simulation experimental results show this system can detect the fatigue state of drivers effectively,and its performance is obviously improved by the optimization algorithm.
The single or similar feature index in fatigue detection is easily disturbed,so a fatigue detection system based on multi-class feature information fusion is proposed according to the regression analysis and fuzzy evaluation theory. In accordance with the features of each fatigue characteristic,the feature parameters are described and extracted. The quantitative classification of fatigue degree is performed in combination with PVT test. The advantages of regression analysis for explaining the multivariate influence intensity and fuzzy mathematics for dealing with the uncertain problems are used to complete the design and modeling of the detection system. An optimization algorithm is proposed to overcome the interference factors in image feature extraction.The simulation experimental results show this system can detect the fatigue state of drivers effectively,and its performance is obviously improved by the optimization algorithm.
引文
[1]孙伟,张为公,张小瑞,等.疲劳驾驶检测方法的研究进展[J].汽车技术,2009(2):1-5.SUN Wei,ZHANG Weigong,ZHANG Xiaorui,et al. Development of fatigue driving detection method research[J]. Automobile technology,2009(2):1-5.
[2]聂尔豪,苏维均,于重重,等.车载疲劳驾驶行为检测系统的研究与设计[J].计算机仿真,2013,30(8):173-176.NIE Erhao,SU Weijun,YU Chongchong,et al. Research and design of fatigue driving behavior system in vehicle[J]. Computer simulation,2013,30(8):173-176.
[3] ZHENG X L,LI X S,CHENG Z Q,et al. Analysis of driver visual characteristics based on FaceLAB[J]. Applied mechanics and materials,2014,526:283-287.
[4]李娟,王富,王维锋,等.基于数据融合的疲劳驾驶检测算法[J].武汉工程大学学报,2016,38(5):505-510.LI Juan,WANG Fu,WANG Weifeng,et al. Research on dangerous driving behavior recognition and vehicle tracking algorithm based on data fusion[J]. Journal of Wuhan Institute of Technology,2016,38(5):505-510.
[5]谢承玲,陈彩明,刘堂宇.一种车载疲劳驾驶监测仪[J].现代电子技术,2013,36(12):121-123.XIE Chengling, CHEN Caiming, LIU Tangyu. A vehiclemounted fatigue driving monitor[J]. Modern electronics technique,2013,36(12):121-123.
[6]李俊峰.基于OPENCV的人眼检测算法研究[J].科技经济导刊,2017(24):37.LI Junfeng. Research on eye detection based on OPENCV[J].Technology and economic guide,2017(24):37.
[7]李春杰,王春,王全虎.基于嵌入式平台人眼疲劳检测终端的设计与实现[J].现代电子技术,2016,39(24):6-8.LI Chunjie,WANG Chun,WANG Quanhu. Design and implementation of human eye fatigue detection terminal based on embedded platform[J]. Modern electronics technique,2016,39(24):6-8.
[8]胡玉峰.基于OpenCV的嵌入式人眼追踪系统研究[J].电子科技,2014,27(9):58-61.HU Yufeng. Research on embedded eye tracking system based on OpenCV[J]. Electronic science and technology,2014,27(9):58-61.
[9]邹昕彤.基于表情与头部状态识别的疲劳驾驶检测算法的研究[D].长春:吉林大学,2017.ZOU Xintong. Research on fatigue driving detection algorithm based on expression and head state recognition[D]. Changchun:Jilin University,2017.
[10] SOMMER D,GOLZ M. Evaluation of PERCL-OS based current fatigue monitoring technologies[C]//2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires:IEEE,2010:4456.
[2]聂尔豪,苏维均,于重重,等.车载疲劳驾驶行为检测系统的研究与设计[J].计算机仿真,2013,30(8):173-176.NIE Erhao,SU Weijun,YU Chongchong,et al. Research and design of fatigue driving behavior system in vehicle[J]. Computer simulation,2013,30(8):173-176.
[3] ZHENG X L,LI X S,CHENG Z Q,et al. Analysis of driver visual characteristics based on FaceLAB[J]. Applied mechanics and materials,2014,526:283-287.
[4]李娟,王富,王维锋,等.基于数据融合的疲劳驾驶检测算法[J].武汉工程大学学报,2016,38(5):505-510.LI Juan,WANG Fu,WANG Weifeng,et al. Research on dangerous driving behavior recognition and vehicle tracking algorithm based on data fusion[J]. Journal of Wuhan Institute of Technology,2016,38(5):505-510.
[5]谢承玲,陈彩明,刘堂宇.一种车载疲劳驾驶监测仪[J].现代电子技术,2013,36(12):121-123.XIE Chengling, CHEN Caiming, LIU Tangyu. A vehiclemounted fatigue driving monitor[J]. Modern electronics technique,2013,36(12):121-123.
[6]李俊峰.基于OPENCV的人眼检测算法研究[J].科技经济导刊,2017(24):37.LI Junfeng. Research on eye detection based on OPENCV[J].Technology and economic guide,2017(24):37.
[7]李春杰,王春,王全虎.基于嵌入式平台人眼疲劳检测终端的设计与实现[J].现代电子技术,2016,39(24):6-8.LI Chunjie,WANG Chun,WANG Quanhu. Design and implementation of human eye fatigue detection terminal based on embedded platform[J]. Modern electronics technique,2016,39(24):6-8.
[8]胡玉峰.基于OpenCV的嵌入式人眼追踪系统研究[J].电子科技,2014,27(9):58-61.HU Yufeng. Research on embedded eye tracking system based on OpenCV[J]. Electronic science and technology,2014,27(9):58-61.
[9]邹昕彤.基于表情与头部状态识别的疲劳驾驶检测算法的研究[D].长春:吉林大学,2017.ZOU Xintong. Research on fatigue driving detection algorithm based on expression and head state recognition[D]. Changchun:Jilin University,2017.
[10] SOMMER D,GOLZ M. Evaluation of PERCL-OS based current fatigue monitoring technologies[C]//2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. Buenos Aires:IEEE,2010:4456.