Enhancing Automative Safety System for Driver Fatigue Detection by Using Mediapipe’s Framework

Enhancing Automative Safety System for Driver Fatigue Detection by Using Mediapipe’s Framework

1Induja V.,  2Dr.Tharani S.

1M.Sc., PG Student in Computer Science, Auxilium College (Autonomous), Vellore - 06

2Assistant Professor,Department of Computer Science and Applications, Auxilium College  (Auotonomous),  vellore -06

ABSTRACT

Traffic accidents caused by tiredness, exhaustion, and distracted driving are a big concern on a global scale.  As per global Road Safety reports drowsy driving was reason for 91,000 road accidents in Tamil Nadu in the year 2023.  In this study, I suggest a collective deep learning architecture for automatic driver sleepiness detection  while some earlier works  are suggested extracting the lips and eyes movements to identify sleepiness, more contemporary computer vision-based systems have performed only somewhat well.  This is because they either use extremely large deep learning models with still-poor performance or they use hand-crafted features with traditional methods like K-NN Algotithm and openCV.  To overcome  such issues in this project I am planing to  create a Driver Drowsiness Detection and Alerting System using Mediapipe‘s Face Mesh solution in Python. These systems assess the driver‘s alertness and warn the driver if needed.  Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. we will create a drowsy driver detection system to address such an issue.  For this, we will use Mediapipe‘s Face Mesh solution in python and the Eye Aspect ratio formula using by Navie Bayes Algorithm.  My goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time.

Keyword: Navie Baye’s, Mediapipe Framework.