Drowsiness Detection and Real-Time System for Monitoring Driver Vigilance Using Raspberry Pi

This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviours that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. Transportation is widely used to allow user travel conveniently from place to place, for a personal of official purpose. Travel during peak hour or holiday, expose the driver to traffic jam for several hour, thus cause the drive to feel drowsy easily due to high concentration and lack of rest. This situation contributes the increasing of the percentage of car incident due to car driver fatigue is the primary origin of the car accident. This Image detection drowsiness system is includes with that six parameters which is used to detect the state of the car driver using Eye Aspect Ratio (EAR) technique. A developed system that is used to detect and analyse continuously the state of eye closure in real time. This system able to recognize whether the driver is drowsy or not, with the initial, wearing spectacles, dim light and microsleep condition experimental conducted successfully give 90% of accuracy. This situation can increase the vigilant of drivers significantly.