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System for Detecting Driver`s Drowsiness Robust Variations of External Illumination
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 Title & Authors
System for Detecting Driver`s Drowsiness Robust Variations of External Illumination
Choi, WonWoong; Pan, Sung Bum; Shin, Ju Hyun;
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In this study, a system is proposed for analyzing whether driver`s eyes are open or closed on the basis of images to determine driver`s drowsiness. The proposed system converts eye areas detected by a camera to a color space area to effectively detect eyes in a dark situation, for example, tunnels, and a bright situation due to a backlight. In addition, the system used a thickness distribution of a detected eye area as a feature value to analyze whether eyes are open or closed through the Support Vector Machine(SVM), representing 90.09% of accuracy. In the experiment for the images of driver wearing glasses, 83.83% of accuracy was obtained. In addition, in a comparative experiment with the existing PCA method by using Eigen-eye and Pupil Measuring System the detection rate is shown improved. After the experiment, driver`s drowsiness was identified accurately by using the method of summing up the state of driver`s eyes open and closes over time and the method of detecting driver`s eyes that continue to be closed to examine drowsy driving.
Safe Driving System;Driver`s Drowsiness;External Illumination;State of Driver`s Eyes Open and Close;SVM;Drowsiness Decision;
 Cited by
얼굴 특징점 기반의 졸음운전 감지 알고리즘,오미연;정유수;박길흠;

한국멀티미디어학회논문지, 2016. vol.19. 11, pp.1852-1861 crossref(new window)
J.M. Choi, H. Song, S.H. Park, and C.D. Lee, “Implementation of Driver Fatigue Monitoring System," The Journal of Korean Institute of Communications and Information Sciences, Vol. 37, No. 8, pp. 711-720, 2012. crossref(new window)

I.K. Park, K.S. Kim, S.C. Park, and H.R. Byun, “An Illumination-Robust Driver Monitoring System Based on Eyelid Movement Measurement," Journal of Korean Institute of Information Scientists and Engineers, Vol. 34, No. 3, pp. 255-265, 2007.

Y.M. Baek, G.G. Lee, W.Y. Kim, “Nearby Vehicle Detection in the Adfacent Lane using In-vehicle Front View Camera”, Journal of Korea Multimedia Society, Vol. 15, No. 8, pp. 996-1003, 2012. crossref(new window)

R.N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm," Institute of Electrical and Electronics Engineers Transactions on Biomedical Engineering, Vol. 58, No. 1, pp. 121-131, 2011.

M. Blanco, J.L. Bocanegra, J.F. Morgan, G.M. Fitch, A. Medina, and R.P. Zimmeramnn, Assessment of a Drowsy Driver Warning System for Heavy-Vehicle Drivers : Final Report, DOT HS 811 117, National Highway Traffic Safety Administration, 2009.

H. Song, J.M. Choi, C.D. Lee, B.H. Choi, and J.S. Yoo, "Implementaion of a Safe Driving Assistance System and Doze Detection," The Institute of Electronics Engineers of Korea –Signal Processing, Vol. 49, No. 3, pp. 30-39, 2012.

M.H. Yang, N. Ahuja, and D. Kriegman, "Face Recognition Using Kernel Eigenfaces," Proceeding of International Conference on Image Processing Proceedings, Vol. 1, pp. 37-40, 2000.

J.H. Lee, Method of Identification Eye Status for Driver Drowsiness Monitoring System, Master's Thesis of Ulsan University of Technology, 2015.

P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proceeding of Institute of Electrical and Electronics Engineers Conference on Computer Vision and Patten Recognition, Vol. 1, pp. 511-518, 2001.

K.R. Kim, K.S. Son, J.Y. Ha, S.C. Kim, and B.S. Kang, "Novel Auto White Balance Algorithm Using Adaptive Color Sampling Based on CIE L*a*b*Color Space for Mobile Phone Camera," Journal of The Korea Institute of Information and Communication Engineering, Vol. 12, No. 8, pp. 1356-1362, 2008.

J.H. Choi, J.S. Park, and S.S. Lee, "A High-Performance and Low-Cost Histogram Equalization Scheme for Full HD Image," Journal of The Korea Institute of Information and Communication Engineering, Vol. 15, No. 5, pp. 1147-1154, 2011. crossref(new window)

H.J. Kim, M.W. Ryu, and S.H. Lee, "Lane Detection System for Self-Driving Car," Journal of The HCI Society of Korea, pp. 205-209, 2008.

C.C. Chang and C.J. LIN, http://www.csie.ntu., Version 3.20 released on 2015.

Q. Ji and X. Yang, "Real-time Eye, Gaze, and Face pose Tracking for Monitoring Driver Vigilance," Journal of Real-Time Imaging, Vol. 8, Issue 5, pp. 357-377, 2002. crossref(new window)

T. Hayami, K. Matsunaga, K. Shidoji, and Y. Matsuki "Detecting Drowsiness While Driving by Measuring Eye Movement - A pilot study," Proceeding of The Institute of Electrical and Electronics Engineers 5th International Conference on Intelligent Transportation Systems, pp. 156-161, 2002.