Fatigue Detection in Drivers using Eye-Blink and Yawning Analysis
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International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2017 by IJCTT Journal | ||
| Volume-50 Number-2 |
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| 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.
Abstract -
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%.
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Keywords
Fatigue detection, Yawning Eye-blink, Support Vector Machine, Adaboost.