Abstract :

Driver Drowsiness Is One Of The Leading Causes Of Road Accidents Worldwide, Often Resulting In Severe Injuries And Loss Of Life. Detecting Fatigue At An Early Stage Can Significantly Reduce Accident Rates And Improve Road Safety. This Paper Presents A Real-time Driver Drowsiness Detection System Based On Computer Vision Techniques Using Python And OpenCV. The Proposed System Captures Live Video Through A Webcam, Detects The Drivers Face And Eyes Using Haar Cascade Classifiers, And Continuously Monitors The Eye Aspect Ratio (EAR) To Identify Prolonged Eye Closure. When The EAR Remains Below A Predefined Threshold For A Specified Number Of Consecutive Frames, The System Classifies The Driver As Drowsy And Displays A Visual Alert On The Screen. The Proposed Approach Is Computationally Efficient, Cost-effective, And Does Not Require Wearable Sensors Or Specialized Hardware. Experimental Testing Demonstrates That The System Can Successfully Monitor Driver Alertness Under Normal Lighting Conditions And Provide Timely Warnings To Help Prevent Fatigue-related Accidents. Keywords: Driver Drowsiness Detection, Computer Vision, OpenCV, Haar Cascade Classifier, Eye Aspect Ratio (EAR), Real-Time Monitoring.

Published:

05-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

697 - 702

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite