Real Time Driver Drowsiness Detection with Alert Sound Using Image Processing

Abstract

Drowsy driving is one of the major causes of accident and death. Most of the conventional methods are either vehicle based, behavioral based or psychological based. Major studies have suggested that around 20% of all road accidents are fatigue related. Drowsy Driving can be extremely dangerous, a lot of road accidents are related to the driver falling asleep while driving and subsequently losing control of the vehicle. However, initial signs of fatigue and drowsiness can be detected before a critical situation arises. A direct way of measuring driver fatigue is measuring the state of the driver drowsiness. So it is very important to detect the drowsiness of the driver to save life and property. A low-cost, real-time Driver’s Drowsiness Detection System is developed with high accuracy. The Video and image are extracted using the webcam. In this work, Facial Landmarks are used to detect the eye closure yawn and head. Haar Cascade Algorithm is used in the proposed work to detect objects in images, irrespective of their scale in image and location. This helps to find the status of the closed eyes or opened mouth like yawning, and any frame finds that has hand gestures like nodding or covering opened mouth with hand as innate nature of humans when trying to control the sleepiness.