Driver Drowsiness Monitoring System using Machine Learning

Abstract

Abstract: In today's world, sleepiness is one of the main causes of road accidents, many of which have tragic outcomes. Statistics show that the majority of traffic collisions, which frequently result in fatalities and serious injuries, are caused by sleepy driving. As a result, various studies have been done to develop software that can recognize driver tiredness and alert them before making a major error. Using methods from the automobile industry, several of the more popular ways to design their own systems. However, other factors, such as vehicle type, road design, and the capacity to operate the driver's wheel, significantly impacted these traditional criteria. In order to monitor a driver's drowsiness, certain techniques use psychological methods, which frequently produce the most accurate and dependable results. These methods are expensive, though, because electrodes must be applied to the head and torso. This paper describes a machine learning approach to sleepiness detection. The areas of the driver's eyes are located using face detection, and these regions serve as templates for eye tracking in subsequent frames. Finally, drowsiness detection is performed on the tracked eye images to produce alarm warnings. The three steps of this method are Face detection, Eye detection, and Drowsiness detection. Image processing is used to identify the driver's face, and after that, the image of the driver's eyes is extracted to look for indicators of sleepiness. The HAAR face detection algorithm uses image frames that have been captured as input before producing the detected face. To generate results, the model is provided with a sizable database of closed and open eyes. Every time the driver is observed to be sleepy, Buzz alerts the driver. The suggested method for real-time driver drowsiness detection is a practical and affordable approach.