Drowsiness Detection in live Camera Using Machine Learning

Abstract

The prevalence of accidents caused by driver drowsiness is a significant and pressing issue in today's society. Despite the existence of various drowsiness detection systems, the high incidence of such accidents indicates a need for more accurate and reliable solutions. This research aims to address this problem by developing a Drowsiness Detection system using machine learning and real-time image processing. The proposed system leverages public datasets containing images and videos of drivers under various states of alertness. These datasets are preprocessed and fed into a Convolutional Neural Network (CNN) model for training. The model is designed to detect signs of drowsiness in real-time, providing timely alerts to potentially drowsy drivers. This research represents a comprehensive effort to improve road safety by addressing the issue of driver drowsiness. By utilizing advanced machine learning techniques and real-time image processing, the proposed system aims to provide a more accurate and reliable solution to drowsiness detection. The insights and developments from this research have the potential to significantly reduce the number of accidents caused by driver drowsiness, thereby ensuring safer roads and protecting the lives of drivers and pedestrians alike.