Fatigue Detection in Drivers using Eye-Blink and Yawning Analysis
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2017 by IJCTT Journal|
|Year of Publication : 2017|
|Authors : Ojo, J.A., Omilude, L.T., Adeyemo, I.A.|
|DOI : 10.14445/22312803/IJCTT-V50P115|
Ojo, J.A., Omilude, L.T., Adeyemo, I.A. "Fatigue Detection in Drivers using Eye-Blink and Yawning Analysis". International Journal of Computer Trends and Technology (IJCTT) V50(2):87-90, August 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Colossal loss of lives and economic resources has given rise to the need to develop an active safety system that can prevent road accidents by warning drivers of their poor driving conditions, thus, the emergence of driver monitoring system, especially in automation system of future vehicles. This research work proposes an approach to test driver’s alertness through hybrid process of eye blink detection and yawning analysis. The system counts the number of left and eye blinks as well as yawning detected, and compared with a threshold after which an alarm is triggered to show that fatigue had been detected. The algorithm was implemented in MatLab 8.10 (R2013a) using the detection accuracy, sensitivity, specificity as metrics for performance evaluation. The developed algorithm gave detection accuracy rate of 85.7%, sensitivity rate of 75%, precision rate of 60% and specificity rate of 88.24%.
 S. Abtahi, B. Hariri, S, Shirmohammadi (2011) “Driver Drowsiness Monitoring Based on Yawning Detection”, Proc. IEEE International Instrumentation and Measurement Technology Conference, Binjiang (Hangzhou), China, May 10-12.
 L.M. Bergasa, J. Nuevo, M.A. Soteli, R. Barea, M.E. Lopez (2006) “Real time system for monitoring driver Vigilance”, in IEEE Transactions on Intelligent Transportation Systems, 7 (1).
 M.R. Doering, D. Manstetten, T. Atmueller, U. Lasdstaetter, and M. Mahler (2001): “Monitoring Driver Drowsiness and Stress in a Driving Simulator” Proceedings of International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design.
 W.B. Horng and C.Y. Chen (2008): “A Real-Time Driver Fatigue Detection System Based on Eye Tracking and Dynamic Template Matching”, Tamkang Journal of Science and Engineering, 11 (1), pp.65-72.
 S.G. Klauer, T.A. Dingus, V.L. Neale, J.D. Sudweeks, & D.J. Ramsey (2006): “The impact of driver inattention on near-crash/crash risk” An analysis using the 100-Car Naturalistic Driving Study data.
 M.H. Sigari, M. Fathy and M. Soryani (2012): "Designing a driver`s face monitoring system for driver`s fatigue and distraction detection", Rahvar, 9 (18), pp. 39-51.
 P. Viola and M. Jones (2001): "Robust real-time face detection," International Journal of Computer Vision, 57 (2).
 M.S.B. Zainal, I. Khan, & H. Abdullah (2014). Efficient drowsiness detection by facial features monitoring. Research Journal of Applied Sciences, Engineering and Technology, 7(11), 2376-2380.
Fatigue detection, Yawning Eye-blink, Support Vector Machine, Adaboost.