Raspberry Pi Based Driver Alertness Monitoring Using Facial Landmark Analysis

Abdurahman - Abdurahman, Ahmad Zarkasi, M Aren Saputra, Sutarno Sutarno, Aditya P. P. Prasetyo, Sarmayanta Sembiring

Abstract

Drowsiness while driving is one of the conditions that can reduce a driver’s concentration and alertness; therefore, an early warning system capable of directly monitoring the driver’s condition is required. This study aims to design a drowsiness detection system for four-wheeled vehicle drivers based on Raspberry Pi using the Facial Landmark Detection (FLD) method with the Eye Aspect Ratio (EAR) parameter. The system is designed by utilizing a webcam as the input device to capture the driver’s facial image, Raspberry Pi as the data processing unit, a buzzer as the warning output device, and a push button as the manual reset button. The detection process is carried out using MediaPipe Face Mesh to obtain landmark points in the eye area. The EAR value is used to determine whether the eyes are open or closed, while the number of eye blinks is used as an indicator to determine the driver’s drowsiness condition. Testing was conducted by measuring EAR values at various distances and face positions, as well as by testing the Eye Blink Counter by comparing the number of blinks calculated by the system with manual observations. The test results show that the system is able to detect faces and eye landmarks in most testing conditions with distance variations ranging from 10 to 95 centimeters. In the Eye Blink Counter test conducted 24 times with a duration of 60 seconds, an average error of 2.49 percent was obtained.

Keywords

Drowsiness Detection, Raspberry Pi, Facial Landmark Detection, Eye Aspect Ratio

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References

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DOI: https://doi.org/10.20961/jeeict.8.2.121289

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