Drivers Real time Drowsiness, Attention Detection and Alarm System using Eye Aspect Ratio(EAR) Analysis

In contemporary settings, drowsiness and lack of attention pose significant risks, particularly in critical environments such as driving and machinery operation. This project proposes a real-time drowsiness and attention detection system utilizing the Eye Aspect Ratio (EAR) to monitor and alert individuals about potential drowsiness or lack of focus. Leveraging a Raspberry Pi as the controller and a web camera, the system continuously captures and analyzes eye movements to calculate the EAR, a measure indicative of eye closure. The data is processed to detect prolonged eye blinks or reduced eye aspect ratios, which are indicative of drowsiness or inattentiveness.The system employs an LCD to display real-time feedback and a buzzer to provide auditory alerts when drowsiness is detected. Additionally, a DC motor is integrated to simulate vehicle control, stopping the vehicle if the driver is detected to be asleep. When drowsiness is detected, the web camera captures an image, and a speaker provides audible sound alerts. The integration of these components ensures a comprehensive and responsive alert mechanism. By utilizing advanced image processing techniques, real-time monitoring, and automated control, this system aims to enhance safety and operational efficiency, mitigating the risks associated with drowsiness and lack of attention in critical scenarios, such as driving.

.Keywords: Raspberry Pi, web camera, drowsiness