International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 13, No. 3, 8 Jun. 2023
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Real Time Implementation of Driver Drowsiness Monitoring System Using SVM Classifier
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Author(s)
Index Terms
Aspect Ratio (EAR), Mouth Aspect Ratio (MOR), Raspberry Pi, Support Vector Machine (SVM) etc
Abstract
Nowadays, as transportation is increasing day by day and the probability of occurance of it in future is also very high. There are so many people travel an hours together every day, due to lack of rest the driver may feel tired or drowsy and may fall asleep. This may lead to several highway calamities causing to severe injuries, loss of human life etc. So solve this issue we propose a driver drowsiness monitoring system that helps in avoiding major accidents. The proposed method detects the status of the driver of the vehicle using the Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR) techniques. The developed system includes a Pi camera, Raspberry Pi module and is used to detect and analyze continuously the eye closure status in real-time. When drowsiness is detected buzzer sound will alert the driver which significantly helps in reducing the percentage of highway calamities by alerting.
Cite This Paper
Ashwini Araballi, Sangharsh Shinde, "Real Time Implementation of Driver Drowsiness Monitoring System Using SVM Classifier", International Journal of Engineering and Manufacturing (IJEM), Vol.13, No.3, pp. 48-54, 2023. DOI:10.5815/ijem.2023.03.05
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