A Real-Time Driver Drowsiness Detection System Using MediaPipe and Eye Aspect Ratio

TL;DR

This paper presents a real-time, low-cost driver drowsiness detection system using MediaPipe and Eye Aspect Ratio, which accurately identifies drowsiness signs through facial landmark tracking and alerts drivers promptly.

Contribution

It introduces a lightweight, real-time drowsiness detection system leveraging MediaPipe and EAR, enhancing road safety with high accuracy and quick response.

Findings

High detection accuracy demonstrated in experiments

System responds quickly to signs of drowsiness

Low-cost implementation suitable for real-time use

Abstract

One of the major causes of road accidents is driver fatigue that causes thousands of fatalities and injuries every year. This study shows development of a Driver Drowsiness Detection System meant to improve the safety of the road by alerting drivers who are showing signs of being drowsy. The system is based on a standard webcam that tracks the facial features of the driver with the main emphasis on the examination of eye movements that can be conducted with the help of the Eye Aspect Ratio (EAR) method. The Face Mesh by MediaPipe is a lightweight framework that can identify facial landmarks with high accuracy and efficiency, which is considered to be important in real time use. The system detects the moments of long eye shutdowns or a very low rate of blinking which are manifestations of drowsiness and alerts the driver through sound to get her attention back. This system achieves a high-performance and low-cost driver monitoring solution with the help of the computational power of OpenCV to process the image and the MediaPipe to identify faces. Test data experimental analyses indicate that the system is very accurate and responds quicker; this confirms that it can be a component of the current Advanced Driving Assistance System (ADAS).