Article information
Resumen
En este artículo se presenta un sistema avanzado de asistencia a la conducción (SAAC) diseñado para detectar automáticamente a somnolencia y la distracción del conductor. Este sistema se compone de dos partes: una para trabajar durante el día con luminación natural, y otra para funcionar en la noche utilizando iluminación infrarroja. Los principales objetivos son localizar l rostro y los ojos del conductor para analizarlos a través del tiempo y generar un índice de somnolencia y uno de distracción. Para llo se han utilizado técnicas de Visión por Computador e Inteligencia Artificial. Finalmente, el sistema ha sido probado con varios onductores sobre un vehículo en condiciones reales de conducción, en el día y en la noche.
Palabras clave:
Inteligencia Artificial
Visión por Computador
somnolencia
distracción
conductor
accidentes de tráfico
iluminación infrarroja
Full text is only aviable in PDF
Referencias
[ASFA, 2008]
ASFA, 2008. Driver fatigue is the number one cause of catastrophic truck accidents. Website, http://www.autoroutes.fr/.
[Bergasa et al., 2006]
L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, E. Lopez.
Real-time system for monitoring driver vigilance.
IEEE, Transactions on Intelligent Transportation Systems, 7 (2006 March), pp. 63-77
[Bergasa et al., 2004]
Bergasa, L., Nuevo, J., Sotelo, M., Vásquez, M., Jun 14-17 2004. Realtime system for monitoring driver vigilance. IEEE, Intelligent Vehicles Symposium 1.(2).
[Bloemkolk et al., 2007]
Bloemkolk, F., de Lijster, J., van Gelderen, M., July 2007. ITS strategy: the japanese formula for success. Study to promote ITS implementation in the Netherlands. Technical report, International A_aris O_ce, Ministry of Transportation, Public Works and Water Management.
[Branzan et al., 2008]
Branzan, A., Widsten, B., Wang, T., Lan, J., Mah, J., June 2008. A computer vision-based system for real-time detection of sleep onset in fatigued drivers. IEEE, Intelligent Vehicles Symposium, 25-30.
[Brookshear, 1983]
Brookshear, J., 1983. Theory of computation: Formal Languages; Automata and Complexity. Vol. 1. Addison Wesley Iberoamericana.
[Chang et al., 2007]
Chang, B., Lim, J., Kim, H., Seo, B., September 2007. A study of classification of the level of sleepiness for the drowsy driving prevention. IEEE, SICE Annual Conference, 3084-3089.
[Cristianini and Shawe-Taylor, 2006]
N. Cristianini, J. Shawe-Taylor.
An introduction to Support Vector Machines and other kernel-based learning methods.
Cambrige University Press, (2006),
[Daugman, 1985]
J. Daugman.
Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortial filters.
J. Optical Soc. Am., 2 (1985), pp. 1160-1169
[de la Escalera, 2001]
de la Escalera, A., 2001. Visión por Computador, Fundamentos y Métodos. Vol. 1. Prentice Hall, Pearson Educación, Madrid.
[Dong and Wu, 2005]
Dong,W.,Wu, X., 2005. Driver fatigue detection based on distant eyelid. IEEE, Int. Workshop VLSI Design & Video Tech.
[Doucet et al., 2001]
Doucet, A., N. Freitas de, Gordon, N., 2001. Sequential Monte Carlo Methods in Practice. Vol. 1. Springer-Verlag.
[D‘Orazio et al., 2004]
D‘Orazio, T., Leo, M., Distante, A., June 2004. Eye detection in face images for a driver vigilance system. IEEE, Intelligent Vehicle Symposium, 95-98.
[Durrett, 1991]
Durrett, R., 1991. Probability: Theory and Examples. Vol. 1. Library of Congress Catalogingin-Publication Data.
[Evgeniou et al., 2000]
Evgeniou, T., Pontil, M., Papageorgiou, C., Poggio, T., 2000. Image representations for object detection using kernel classifiers. In Asian Conference on Computer Vision.
[Fletcher et al., 2003]
Fletcher, L., Petersson, L., Zelinsky, A., 2003. Driver assistance systems based on vision in and out of vehicles. IEEE, Proceedings of Intelligent Vehicle Symposium, 322-327.
[Freund and Schapire, 1995]
Freund, Y., Schapire, R., 1995. A decision-theorical generalization of online learning and an application to boosting. In Second European Conference on Computational Learning Theory.
[Gejgus and Sperka, 2003]
Gejgus, P., Sperka, M., 2003. Face tracking in color video sequences. Association for Computing Machinery, 245-249.
[Grace et al., 1998]
R. Grace, V. Byrne, D. Bierman, J. Legrand, D. Grcourt, R. Davis, J. Staszewski, B. Carnahan.
A drowsy driver detection system for heavy vehicles.
IEEE, Proceedings of Digital Avionics System Conference, 2 (1998 Octuber), pp. 1-8
[Guo and Guo, 2009]
Guo, J., Guo, X., 2009 July. Eye state recognition based on shape analysis and fuzzy logic. IEEE Intelligent Vehicle Symposium, 78-82.
[Hagenmeyer, 2007]
Hagenmeyer, L., August 2007. Development of a multimodal, universal human-machine-interface for hypovigilance-management-systems. Ph.D. thesis, Mechanical Engineering, University of Stuttgart, Institute for Human Factors and Technology Management.
[Hanmi, 2005]
Hanmi, I., 2005. Drowsy truck drivers. Website, http://www.gohanmi.com/NREC-COPILOT.htm.
[Hansen and Ji, 2010]
D. Hansen, Q. Ji.
In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence (3), (2010 March), pp. 478-500
[Hayami et al., 2002]
Hayami, T., Matsunaga, K., Shidoji, K., Matsuki, Y., September 2002. Detecting drowsiness while driving by measuring eye movement - a pilot study. IEEE International Conference on Intelligent Transportation Systems, 156-161.
[Hilario, 2008]
Hilario, C., Oct 2008. Detección de peatones en el espectro visible einfrarrojo para un sistema avanzado de asistencia a la conducción. Ph.D. thesis, Departamento de Ingeniería de Sistemas y Automática, Universidad. Carlos III de Madrid.
[Horng et al., 2004]
Horng, W., Chen, C., Chang, Y., 2004. Driver fatigue detection based on eye tracking and dynamic template matching. IEEE Proceedings of, International Conference on Networking, Sensing and Control.
[Isard and Blake, 1998]
M. Isard, A. Blake.
Condensation - conditional density propagation for visual tracking.
International Journal of Computer Vision, 29 (1998), pp. 5-28
[Isard, 1998]
Isard, M.A., September 1998. Visual motion analysis by probabilistic propagation of conditional density. Ph.D. thesis, Department of Engineering Science, University of Oxford.
[Ji and Yang, 2001]
Q. Ji, X. Yang.
Real-time visual cues extraction for monitoring driver vigilance.
Lectures Notes in Computer Science, Proceedings of the Second International Workshop on Computer Vision Systems, 2095 (2001), pp. 107-124
[Ji and Yang, 2002]
Q. Ji, X. Yang.
Real-time eye, gaze and face pose tracking for monitoring driver vigilance.
Elsevier Science Ltd., Real Time Imaging, 1 (2002), pp. 357-377
[Ji et al., 2004]
Ji, Q., Zhu, Z., Lan, P., Jun 2004. Real time nonintrusive monitoring and prediction of driver fatigue. IEEE, Transaction on Vehicular Technology 53.(4).
[Jiangwei et al., 2004a]
Jiangwei, C., Lisheng, J., Lie, G., Keyou, G., Rongben,W., June 2004a. Driver's eye state detecting method design based on eye geometry feature. IEEE, Intelligent Vehicles Symposium, 357-362.
[Jiangwei et al., 2004b]
Jiangwei, C., Lisheng, J., Lie, G., Keyou, G., Rongben, W., June 2004b. A monitoring method of driver mouth behaviour based on machine vision. IEEE, Intelligent Vehicles Symposium, 351-356.
[Knipling and Wierwille, 1994]
Knipling, R., Wierwille, W., 1994. Vehicle-based drowsy driver detection: Current status and future prospects. IVSH America Fourth Annual Meeting. Koller-Meier, E., Ade, F., ???? Tracking multiple objects using the condensation algorithm.
[Kücükay and Bergholz, 2005]
Kücükay, F., Bergholz, J., 2005. Driver assistant systems. Lectures of Institute of Automatic Engineering.
[Kutila, 2006]
Kutila, M., Dicember 2006. Methods for machine vision based driver monitoring applications. Ph.D. thesis, Tietotalo Building, Auditorium TB104.
[Lisheng et al., 2009]
Lisheng, J., Xuan, S., Yuying, J., Haijing, H., Yuqin, S., June 2009. Study on driver's mouth segmentation and location based on color space. IEEE Intelligent Vehicles Symposium, 500-506.
[Liu, 2004]
Liu, C., May 2004. Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligent 26 (5), 572-582.
[Longhurst, 2011]
Longhurst, G., ???? Understanding driver visual behaviour. Seeing Machine Pty Limited.
[Looney, 1997]
C. Looney.
Pattern Recognition Using Neural Networks: theory and algorithms for engineers and scientists.
Oxford University Press Inc, (1997),
[Loy, 2003]
Loy, G., January 2003. Computer vision to see people: a basis for enhanced human computer interaction. Ph.D. thesis, Robotics Systems Laboratory, Department of Systems Engineering, Research School of Information Sciences and Engineering, Australian National University.
[Loy and Barnes, 2004]
G. Loy, N. Barnes.
Fast shape-based road sign detection for a driver assistance system.
IEEE, International Conference on Intelligent Robots and Systems (IROS’04), 1 (2004 September), pp. 70-75
[Loy and Zelinsky, 2003]
G. Loy, A. Zelinsky.
August. Fast radial symmetry for detecting points of interest. IEEE, Transactions on Pattern Analysis and Machine Intelligence, 25 (2003), pp. 959-973
[Martinez and Martinez, 2002]
Martinez, W., Martinez, A., 2002. Computational Statistics Handbook with Matlab. Chapman & Hall=CRC. NHTSA, April 1998. Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management. Final Report DOT HS 808 762, National Highway Tra_c Safety Administration, Virginia. [22161,] USA.
[Otsu, 1979]
Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics, 62-66.
[Pitas, 2000]
I. Pitas.
Digital Image Processing Algorithms and Applications. A Wiley-Interscience Publication.
John Wiley & Sons, Inc, (2000),
[Ristic et al., 2004]
Ristic, B., Arulampalam, S., Gordon, N., 2004. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Vol. 1. Artech House.
[Rogers, 1998]
C. Rogers.
Hausdor_ measures Vol. 1.
Cambridge University Press, (1998),
[Rongben et al., 2003]
Rongben, W., Keyou, G., Shuming, S., Jiangwei, C., June 2003. A monitoring method of driver fatigue behavior based on machine vision. IEEE, Procedings on Intelligent Vehicles Symposium, 110-113.
[Tian and Qin, 2005]
Tian, Z., Qin, H., Octuber 2005. Real-time driver's eye state detection. IEEE, International Conference on Vehicular Electronics and Safety, 285-289.
[Viola and Jones, 2001]
P. Viola, M. Jones.
Rapid object detection using a boosted cascade of simple features.
Computer Vision and Pattern Recognition, Proceedings of the 2001 IEEE Computer Society Conference on, 1 (2001), pp. 1-511
[Viola and Jones, 2002a]
Viola, P., Jones, M., 2002a. Fast and robust classification using asymmetric adaboost and a detector cascade. Advances in Neural Information Processing System, MIT Press, Cambrige, M.A.(14).
[Viola and Jones, 2002b]
Viola, P., Jones, M., 2002b. Robust real-time object detection. International Journal of Computer Vision - to appear.
[Vlacic et al., 2001]
Vlacic, L., Parent, M., Harashima, F., 2001. Intelligent Vehicle Technologies. A division of Reed Educational and Professional Publishing Ltda. Library of Congress Cataloguing in Publication Data.
[Wang et al., 2006]
Wang, Q., Yang, J., Ren, M., Zheng, Y., June 2006. Driver fatigue detection: A survey. IEEE, Proceedings of the 6th World Congress on Intelligent Control and Automation, 8587-8591.
[Wu et al., 2004]
Wu, Y., Liu, H., Zha, H., June 2004. A new method of detection humand eyelids based on deformable templates. IEEE International Conference on Systems, Man and Cybernectics, 604-609.
[Zhou and Wei, 2006]
M. Zhou, H. Wei.
Face verification using gabor wavelets and adaboost.
IEEE, 18th. International Conference on Pattern Recognition ICPR06, 1 (2006), pp. 404-407
[Zhu et al., 2002a]
Z. Zhu, K. Fujimura, Q. Ji.
Real-time eye detection and tracking under various light conditions.
Proceedings of the 2002 Symposium of Eye tracking research & applications, (2002), pp. 139-144
[Zhu et al., 2002b]
Z. Zhu, Q. Ji, K. Fujimura, K. Lee.
Combining Kalman filtering and mean shift for real time eye tracking under active ir illumination. Proceedings of the 16 th International Conference on Pattern Recognition (ICPR’02), 4 (2002), pp. 318-321
Copyright © 2011. Elsevier España, S.L.. Todos los derechos reservados