Automated Driver Drowsiness Detection

Abstract

This project uses image processing and Python programming language to create an automatic sleepiness detection system for drivers. The system's objective is to develop an algorithm that can reliably gauge a driver's sleepiness under various circumstances by using real-time grayscale photographs. The driver will be observed by the system as it scans their face and eyelids for hypo vigilance, which is a precursor to tiredness. To properly determine the driver's level of sleepiness, the system entails three steps: feature selection, eye-pair state detection, and decision-making. The algorithm will differentiate between the two states on a collection of captured photos of both tired and awake drivers. To identify sleepiness in drivers, facial feature recognition algorithms has been employed, with an emphasis on the eye pair. Images will be taken with a mounted camera. For the system to successfully be integrated into current driver assistance systems, it will be measured on how well it is able to identify tiredness. The project's importance stem from its ability is to increase road safety by identifying driver sleepiness, a major contributing factor to accidents on the road. In conclusion, this research will significantly impact road safety by tackling the problem of accidents caused by drowsiness.