Real Time Implementation of Driver Drowsiness Detection for an Intelligent Transportation System

Driver drowsiness is a major cause for traffic accidents globally. This real-time system for an intelligent transportation system can detect the driver drowsiness in factual time. It boosts transportation and road safety. To address this problem, a strong and an effectual Driver Drowsiness Discovery System is projected. The primary objective of the proposed system is to notice the driver's drowsiness, issue alerts to prevent road accidents. The proposed tactic involves some image preprocessing procedures like data collection, noise reduction and feature extraction. Then the application of Eye Aspect Ratio (EAR) algorithm will be used for the facial feature identification. This system practices Eye Aspect Ratio (EAR) to find out the openness of the eye to determine the driver's drowsiness and also it uses the Mouth Aspect Ratio (MAR) find out the openness of the mouth to determine yawning which is also a sign of drowsiness. The proposed system includes different circumstances like normal state, with glasses and without mask, with mask and without glasses, and with both glasses and mask.