Real-Time Warning System for Driver Drowsiness Detection Using Visual Information

Traffic accidents due to human errors cause many deaths and injuries around the world. To help in reducing this fatality, in this research, a new module for Advanced Driver Assistance System (ADAS) for automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate, to track and to analyze the face and the eyes to compute a drowsiness index, working under varying light conditions and in real time. Examples of different images of drivers taken in a real vehicle are shown to validate the algorithm.

[1]  Lorenz Hagenmeyer,et al.  Development of a multimodal, universal human-machine-interface for hypovigilance-management-systems , 2007 .

[2]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[3]  D. Dinges,et al.  EVALUATION OF TECHNIQUES FOR OCULAR MEASUREMENT AS AN INDEX OF FATIGUE AND THE BASIS FOR ALERTNESS MANAGEMENT , 1998 .

[4]  Miguel Ángel Sotelo,et al.  Real-time system for monitoring driver vigilance , 2004, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[5]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[6]  Michael Isard,et al.  Visual Motion Analysis by Probabilistic Propagation of Conditional Density , 1998 .

[7]  J. Glenn Brookshear,et al.  Theory of Computation: Formal Languages, Automata, and Complexity , 1989 .

[8]  Andry Rakotonirainy,et al.  Affordable visual driver monitoring system for fatigue and monotony , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[9]  Carl G. Looney,et al.  Pattern recognition using neural networks: theory and algorithms for engineers and scientists , 1997 .

[10]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[11]  James R. Parker,et al.  Practical Computer Vision Using C , 1993 .

[12]  Qiang Ji,et al.  Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance , 2002, Real Time Imaging.

[13]  Peter Gejgus,et al.  Face tracking in color video sequences , 2003, SCCG '03.

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[15]  José Manuel Pastor,et al.  IVVI: Intelligent vehicle based on visual information , 2007, Robotics Auton. Syst..

[16]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Xiaojuan Wu,et al.  Fatigue detection based on the distance of eyelid , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..

[18]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[19]  Huabiao Qin,et al.  Real-time driver's eye state detection , 2005, IEEE International Conference on Vehicular Electronics and Safety, 2005..

[20]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[21]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[22]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[23]  I.Hussain M. Mujtaba,et al.  Applications of Neural Networks and Other Learning Technologies in Process Engineering , 2001 .

[24]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[25]  Weixing Wang,et al.  Driver Fatigue Detection Based on Eye Tracking , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[26]  Yen-Wei Chen,et al.  A Robust Eye Detection and Tracking Technique Using Gabor Filters , 2007, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007).

[27]  Takeshi Shakunaga,et al.  Multiple target tracking by appearance-based condensation tracker using structure information , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[28]  de Dick Waard,et al.  Proceedings 3rd International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design , 2008 .

[29]  Lars Petersson,et al.  Driver assistance systems based on vision in and out of vehicles , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[30]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[31]  Iyad F. Jafar,et al.  A New Method for Image Contrast Enhancement Based on Automatic Specification of Local Histograms , 2007 .

[32]  Hongbin Zha,et al.  A new method of detecting human eyelids based on deformable templates , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[33]  J. Daugman Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[34]  A.B. Albu,et al.  A computer vision-based system for real-time detection of sleep onset in fatigued drivers , 2008, 2008 IEEE Intelligent Vehicles Symposium.