Driver Drowsiness Detection Using Haar Cascade Classifier

Fatigued drivers are one of the primary causes of traffic accidents. This project proposes a real-time drowsiness detection system that uses the Raspberry Pi, OpenCV, and Haar Cascade classifiers to increase road safety. After a camera module captures video of the driver, image processing identifies important facial cues like eye closure, blink rate, and head orientation. If drowsiness is detected, an alert (light or alarm) is activated to notify the driver. This Raspberry Pi-powered lightweight and reasonably priced system works with all kinds of vehicles, unlike expensive luxury car systems. Through the integration of these technologies, the project aims to reduce the risks associated with sleepy driving and promote safer roads.