期刊文献+

Fatigue driving detection based on Haar feature and extreme learning machine 被引量:5

Fatigue driving detection based on Haar feature and extreme learning machine
原文传递
导出
摘要 As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers' fatigue driving by using the human eye tracking technology. As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers' fatigue driving by using the human eye tracking technology.
出处 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2016年第4期91-100,共10页 中国邮电高校学报(英文版)
基金 supported by the National Natural Science Foundation of China(61272357,61300074,61572075)
关键词 Haar feature extreme learning machine fatigue driving detection Haar feature extreme learning machine fatigue driving detection
  • 相关文献

参考文献2

二级参考文献21

  • 1曹菊英,赵跃龙.基于水平投影和Hough查找圆法的人眼状态识别研究[J].科学技术与工程,2007,7(9):1969-1971. 被引量:6
  • 2TIAN Z C, QIN H B. Real -time driver eye status de- tection[ C ]//Proceedings of International Conference on Vehicular Electronics and Safety. Xian China :IEEE Press, 2005(10) : 285 -289.
  • 3HONG T Y, QIN H B, SUN Q S. An improved real time eye status identifieation system in driver drowsiness de- tection [ C] //Proceedings of International Conference on Control and Automation. Guangzhou China: IEEE Press, 2007(5) : 1449 - 1453.
  • 4Daugman J G. High confidence visual recognition of p,-rsons by a test of statistical independence[ J ]. IEEE Transactions on l"att:'rn Analysis and Machine Intelligence, 1993,15 ( 11 ) : 1148 -1161.
  • 5Dieckmann U, Plankensteiner P, Schamburger R, el al. Sesam: A biometric person identification system using sensor fnsion [ C]// Audio-and Video-based Biometric Person Authentication, Brlin Heidelberg: Springer, 1997:301 -310.
  • 6l,iang L, Xiao R, Wen F, e al. Face alignment via componnt- based discriminative search [ C ]//Computer isim, ECCV 2008. BerLin Heideberg:Springer,2008:72 -85. C.
  • 7ai Q, Gallup D, Zhang C, et al. 3 D detbrmahle Itcc tracking with a commodity depth camera [ C ]// Computer Vision, ECCV 2.010, Berlin Heidelberg:Springer,2010:229 -242.
  • 8Zhou M, Liang l,,Sun j,et al. AAM-based face tracking with tem- poral matching and face segmentation [ C ]//2010 IEEE Confe- rence on Computer Vision amt Patten Recognitim CVPR , 1EEE ,2010:701 -708.
  • 9曹倩霞,罗大庸,李顺.基于眼睛特征跟踪的眼睛状态跟踪[J].计算机测量与控制,2007,15(12):1794-1797. 被引量:1
  • 10成波,张广渊,冯睿嘉,李家文,张希波.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(11):1001-1005. 被引量:14

共引文献6

同被引文献33

引证文献5

二级引证文献39

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部