• SRINIVASARAO UDARA
• M.Naresh2

Fatigue-induced driving is a significant contributor to road accidents and fatalities. Consequently, there is ongoing research into detecting driver fatigue and signaling its onset. Traditional approaches predominantly fall into three categories: vehicle-based, behavioral-based, and physiological-based. Many methods either intrusively disrupt the driver or necessitate costly sensors and complex data processing. Thus, this study focuses on developing a cost-effective, real-time drowsiness detection system with satisfactory accuracy. Within the system architecture, a webcam captures video footage, utilizing image processing techniques to detect the driver's face in each frame. Facial landmarks are then identified, allowing computation of metrics such as eye aspect ratio, mouth opening ratio, and nose length ratio. Drowsiness is determined using adaptive thresholding based on these metrics. Additionally, offline machine learning algorithms have been integrated. The Support Vector Machine based classification achieves a sensitivity of 95.58% and specificity of 100%..

"Realtime Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.c782-c792, December-2024, Available :http://www.jetir.org/papers/JETIR2412290.pdf

"Realtime Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppc782-c792, December-2024, Available at : http://www.jetir.org/papers/JETIR2412290.pdf