• Dr. Ganesh Vilas Patil

Our system aims to address the issue of drowsiness of driver by using Webcam which monitors his face and detect drowsiness. It includes the MediaPipe library, which helps to detect facial detection, including the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) algorithms. The EAR algorithm is used to detect eye landmarks and calculate a ratio that indicates if the driver's eyes are closing, which shows drowsiness. The MAR algorithm, focuses on movements of the driver's mouth such as yawning that shows drowsiness. By continuously observing the driver's face using a webcam it can use these algorithms to detect and analyses eyes and mouth. Based on given conditions, such as a certain EAR or MAR value, the system can determine if the driver is showing symptoms of drowsiness. When drowsiness is detected, the system can activate an alert such as alarm or a through a speaker to alert the driver. This alert serves as a reminder to the driver to stay awake and focused, so that the risk of accidents can be decreased. such systems can be helpful in increasing transportation safety and should be used in with other metrics like regular breaks, sufficient sleep, and using traffic rules.

Drivers Alertness,Eye Aspect Ratio ,drowsiness of driver,Mouth Aspect Ratio

"Detection of Driver’s Alertness", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.e504-e507, September-2023, Available :http://www.jetir.org/papers/JETIR2309461.pdf

"Detection of Driver’s Alertness", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppe504-e507, September-2023, Available at : http://www.jetir.org/papers/JETIR2309461.pdf