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Sistema Avanzado de Asistencia a la Conducción para la Detección de la Somnolencia

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Sistema Avanzado de Asistencia a la Conducción para la Detección de la Somnolencia

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Flores, MJ.; Armingol, JM.; De La Escalera, A. (2011). Sistema Avanzado de Asistencia a la Conducción para la Detección de la Somnolencia. Revista Iberoamericana de Automática e Informática industrial. 8(3):216-228. https://doi.org/10.1016/j.riai.2011.06.009

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Título: Sistema Avanzado de Asistencia a la Conducción para la Detección de la Somnolencia
Otro titulo: Advance assistance system for driver’s drowsiness detection
Autor: Flores, Marco J. Armingol, José M. de la Escalera, Arturo
Fecha difusión:
Resumen:
[ES] 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 ...[+]


[EN] Every day, statistics on traffic accidents reveal that human errors are the main cause of deaths and injuries on the word’s roads. In order to reduce these fatalities, a system for automatic detection of both drowsiness ...[+]
Palabras clave: Artificial intelligent , Computer vision , Drowsiness , Driver , Traffic accidents , Infrared illumination , Inteligencia Artificial , Visión por Computador , Somnolencia , Distracción , Conductor , Accidentes de tráfico , Iluminación infrarroja
Derechos de uso: Reserva de todos los derechos
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.1016/j.riai.2011.06.009
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.riai.2011.06.009
Código del Proyecto:
info:eu-repo/grantAgreement/MEC//TRA2007-67786-C02-02/ES/VISION POR COMPUTADOR PARA LA PERCEPCION DE ENTORNOS VIARIOS/
info:eu-repo/grantAgreement/MEC//TRA2007-67374-C02-01/ES/SISTEMA DE DETECCION DE PEATONES, CICLISTAS Y MOTORISTAS-SISTEMA DE PERCEPCION/
Agradecimientos:
Este trabajo ha sido realizado gracias al apoyo del gobierno español a través de os proyectos de la CICYT, VISVIA (TRA2007-67786-C02-02) y POCIMA (TRA2007-67374-C02-01).
Tipo: Artículo

References

ASFA, 2008. Driver fatigue is the number one cause of catastrophic truck accidents. Website, http://www.autoroutes.fr/.

Bergasa, L. M., Nuevo, J., Sotelo, M. A., Barea, R., & Lopez, M. E. (2006). Real-Time System for Monitoring Driver Vigilance. IEEE Transactions on Intelligent Transportation Systems, 7(1), 63-77. doi:10.1109/tits.2006.869598

Bergasa, L., Nuevo, J., Sotelo, M., Vásquez, M., Jun 14-17 2004. Realtime system for monitoring driver vigilance. IEEE, Intelligent Vehicles Symposium 1.(2). [+]
ASFA, 2008. Driver fatigue is the number one cause of catastrophic truck accidents. Website, http://www.autoroutes.fr/.

Bergasa, L. M., Nuevo, J., Sotelo, M. A., Barea, R., & Lopez, M. E. (2006). Real-Time System for Monitoring Driver Vigilance. IEEE Transactions on Intelligent Transportation Systems, 7(1), 63-77. doi:10.1109/tits.2006.869598

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, 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, 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, J., 1983. Theory of computation: Formal Languages; Automata and Complexity. Vol. 1. Addison Wesley Iberoamericana.

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.

Daugman, J. G. (1985). Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A, 2(7), 1160. doi:10.1364/josaa.2.001160

de la Escalera, A., 2001. Visión por Computador, Fundamentos y Métodos. Vol. 1. Prentice Hall, Pearson Educación, Madrid.

Dong,W.,Wu, X., 2005. Driver fatigue detection based on distant eyelid. IEEE, Int. Workshop VLSI Design & Video Tech.

Doucet, A., N. Freitas de, Gordon, N., 2001. Sequential Monte Carlo Methods in Practice. Vol. 1. Springer-Verlag.

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, R., 1991. Probability: Theory and Examples. Vol. 1. Library of Congress Catalogingin-Publication Data.

Evgeniou, T., Pontil, M., Papageorgiou, C., Poggio, T., 2000. Image representations for object detection using kernel classifiers. In Asian Conference on Computer Vision.

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, 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, P., Sperka, M., 2003. Face tracking in color video sequences. Association for Computing Machinery, 245-249.

Guo, J., Guo, X., 2009 July. Eye state recognition based on shape analysis and fuzzy logic. IEEE Intelligent Vehicle Symposium, 78-82.

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, I., 2005. Drowsy truck drivers. Website, http://www.gohanmi.com/NREC-COPILOT.htm.

Hansen, D. W., & Qiang Ji. (2010). In the Eye of the Beholder: A Survey of Models for Eyes and Gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478-500. doi:10.1109/tpami.2009.30

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, 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, 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, M., & Blake, A. (1998). International Journal of Computer Vision, 29(1), 5-28. doi:10.1023/a:1008078328650

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, Q., & Yang, X. (2001). Real Time Visual Cues Extraction for Monitoring Driver Vigilance. Computer Vision Systems, 107-124. doi:10.1007/3-540-48222-9_8

Ji, Q. (2002). Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance. Real-Time Imaging, 8(5), 357-377. doi:10.1006/rtim.2002.0279

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, 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, 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, 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, F., Bergholz, J., 2005. Driver assistant systems. Lectures of Institute of Automatic Engineering.

Kutila, M., Dicember 2006. Methods for machine vision based driver monitoring applications. Ph.D. thesis, Tietotalo Building, Auditorium TB104.

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, 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, G., ???? Understanding driver visual behaviour. Seeing Machine Pty Limited.

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, G., & Zelinsky, A. (2003). Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), 959-973. doi:10.1109/tpami.2003.1217601

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, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics, 62-66.

Ristic, B., Arulampalam, S., Gordon, N., 2004. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Vol. 1. Artech House.

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, Z., Qin, H., Octuber 2005. Real-time driver's eye state detection. IEEE, International Conference on Vehicular Electronics and Safety, 285-289.

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, P., Jones, M., 2002b. Robust real-time object detection. International Journal of Computer Vision - to appear.

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, 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, 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.

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