Abstract
Every year many people lose their lives due to fatal road accidents around the world and drowsy driving is one of the primary causes of road accidents and deaths. The other causes of traffic accidents are due to human errors and/or due to mechanical failures. Driver fatigue is one of the leading causes of Road Traffic Accidents (RTA) in Pakistan. Numerous systems are invented that minimize the impact of these accidents. The research in this area began sixty years ago, to determine the drowsiness of driver using computer assisted techniques. In this paper, we have purposed a method that will detect the drowsiness of driver by its eye behavior, such as, eye blink rate and patterns. This paper presents a system for detecting driver drowsiness, based on analysis of the eyes. The system has the ability to adapt to any person, works in real driving conditions, under varying lighting and generating drowsiness index at every moment, which measures the wakefulness of the driver. In several experiments, the proposed system has shown excellent results regarding the objectives and the problems have been successfully overcome.
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Sharma, B.R.: Road traffic injuries: a major global public health crisis. Public Health 122(12), 1399–1406 (2008)
Stevenson, M.R., Palamara, P.: Behavioural factors as predictors of motor vehicle crashes: differentials between young urban and rural drivers. Aust. N. Z. J. Public Health 25(3), 245–249 (2001)
Liu, C., Subramanian, R.: Factors Related to Fatal Single-Vehicle Run-Off-Road Crashes, U.S. Department of Transportation, American National Highway Traffic Safety Administration, DOT HS 811 232, Washington, D.C., November (2009)
Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012)
Wang, J.-Q., et al.: Longitudinal collision mitigation via coordinated braking of multiple vehicles using model predictive control. Integr. Comput. Aided Eng. 22(2), 171–185 (2015)
World Health Organization: Global status report on road safety 2015. World Health Organization (2015)
Azam, K., et al.: Comparison of fatigue related road traffic crashes on the national highways and motorways in Pakistan. J. Eng. Appl. Sci. 33, 2 (2014)
Salih, J.H.K., Kulkarni, L.: Fatigue detection system for the drivers using video analysis of facial expressions. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), IEEE (2017)
Liang, Y., Reyes, M.L., Lee, J.D.: Real-time detection of driver cognitive distraction using support vector machines. IEEE Trans. Intell. Transp. Syst. 8(2), 340–350 (2007)
Galarza, E.E., Egas, F.D., Silva, F.M., Velasco, P.M., Galarza, E.D.: Real time driver drowsiness detection based on driver’s face image behavior using a system of human computer interaction implemented in a smartphone. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 563–572. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_53
Eskandarian, A., Ali, M.: Evaluation of a smart algorithm for commercial vehicle driver drowsiness detection. In: 2007 IEEE Intelligent Vehicles Symposium, IEEE (2007)
Prajapati, N.G., et al.: Driver drowsiness detection with audio-visual warning. Int. J. 3, 294–300 (2016)
Sharma, N., Banga, V.K.: Drowsiness warning system using artificial intelligence. World Acad. Sci. Eng. Technol. 4(7), 1771–1773 (2010)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Viola, P., Michael, J.: Rapid object detection using a boosted cascade of simple features. CVPR 1(1), 511–518 (2001)
Baltrušaitis, T., Peter, R., Louis-Philippe, M.: Openface: an open source facial behavior analysis toolkit. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE (2016)
Soukupova, T., Jan, C.: Eye Blink Detection Using Facial Landmarks. 21st Computer Vision Winter Workshop, Rimske Toplice, Slovenia (2016)
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Arif, M., Khan, K.B., Fiaz, K., Niaz, A. (2020). A Real-Time Driver Drowsiness Detection and Warning System Based on an Eye Blinking Rate. In: Bajwa, I., Sibalija, T., Jawawi, D. (eds) Intelligent Technologies and Applications. INTAP 2019. Communications in Computer and Information Science, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-5232-8_10
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DOI: https://doi.org/10.1007/978-981-15-5232-8_10
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