Sistema avanzado de asistencia a la conducción para la detección de la somnolencia

En este articulo se presenta un sistema avanzado de asistencia a la conduccion (SAAC) disenado para detectar automaticamente a somnolencia y la distraccion del conductor. Este sistema se compone de dos partes: una para trabajar durante el dia con luminacion natural, y otra para funcionar en la noche utilizando iluminacion infrarroja. Los principales objetivos son localizar l rostro y los ojos del conductor para analizarlos a traves del tiempo y generar un indice de somnolencia y uno de distraccion. Para llo se han utilizado tecnicas de Vision por Computador e Inteligencia Artificial. Finalmente, el sistema ha sido probado con varios onductores sobre un vehiculo en condiciones reales de conduccion, en el dia y en la noche.

[1]  Angel R. Martinez,et al.  Computational Statistics Handbook with MATLAB , 2001 .

[2]  Alexander Zelinsky,et al.  Fast Radial Symmetry for Detecting Points of Interest , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  B. Carnahan,et al.  A drowsy driver detection system for heavy vehicles , 1998, 17th DASC. AIAA/IEEE/SAE. Digital Avionics Systems Conference. Proceedings (Cat. No.98CH36267).

[4]  Ronald R Knipling,et al.  Vehicle-based drowsy driver detection : current status and future prospects , 1994 .

[5]  Sun Yu-qin,et al.  Study on driver's mouth segmentation and location based on color space , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[6]  Qiang Ji,et al.  In the Eye of the Beholder: A Survey of Models for Eyes and Gaze , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  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.

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

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[11]  Zhiwei Zhu,et al.  Real-time eye detection and tracking under various light conditions , 2002, ETRA.

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

[13]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[14]  Paul A. Viola,et al.  Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.

[15]  Tomaso A. Poggio,et al.  Image representations for object detection using kernel classifiers , 2000 .

[16]  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).

[17]  Qiang Ji,et al.  Real Time Visual Cues Extraction for Monitoring Driver Vigilance , 2001, ICVS.

[18]  Hong Wei,et al.  Face Verification Using GaborWavelets and AdaBoost , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[19]  Gareth Loy,et al.  Computer vision to see people : a basis for enhanced human computer interaction , 2003 .

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

[21]  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.

[22]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[23]  R. Durrett Probability: Theory and Examples , 1993 .

[24]  Hae-Jin Kim,et al.  A study of classification of the level of sleepiness for the drowsy driving prevention , 2007, SICE Annual Conference 2007.

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

[26]  Gareth Blake Loy,et al.  Fast shape-based road sign detection for a driver assistance system , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[27]  M Kutila Methods for machine vision based driver monitoring applications , 2006 .

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

[29]  Zhiwei Zhu,et al.  Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination , 2002, Object recognition supported by user interaction for service robots.

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

[31]  Wang Rongben,et al.  Driver's eye state detecting method design based on eye geometry feature , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[32]  Jingyu Yang,et al.  Driver Fatigue Detection: A Survey , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[33]  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).

[34]  Esther Koller-Meier,et al.  Tracking multiple objects using the Condensation algorithm , 2001, Robotics Auton. Syst..

[35]  Kazunori Shidoji,et al.  Detecting drowsiness while driving by measuring eye movement - a pilot study , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[36]  A. Distante,et al.  Eye detection in face images for a driver vigilance system , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[37]  Xiaosong Guo,et al.  Eye state recognition based on shape analysis and fuzzy logic , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[38]  Chu Jiang-wei,et al.  A monitoring method of driver fatigue behavior based on machine vision , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[39]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

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

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

[42]  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.

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

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

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

[46]  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..

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

[48]  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.

[49]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[50]  Derick Wood,et al.  Theory of computation , 1986 .