Driver Drowsiness Detection System with OpenCV and Keras

Abstract

Drowsiness and Fatigue of driver’s square measure amongst the many causes of road accidents. Every year, the amounts of deaths increases and fatalities injuries globally. During this paper, a module for Advanced Driver help System (ADAS) is given to cut back the quantity of accidents because of driver’s fatigue and thus increase the transportation safety; this technique deals with automatic driver somnolence detection supported visual info and computing. we tend to propose associate degree algorithmic program to find, track, and analyze each the drivers face and eyes to live PERCLOS, a scientifically supported live of somnolence related to slow eye closure. However, the development of such systems encounters many difficulties related to fast and proper recognition of a driver’s fatigue symptoms. One of the technical possibilities to implement driver drowsiness detection systems is to use the visionbased approach. This article presents the currently used driver drowsiness detection systems. The technical aspects of using the vision system to detect a driver drowsiness are also discussed. The parameters of the eyes and mouth detection are created within the face image. The video were change into images frames per second. From there, locating the eyes and mouth can be performed. Once the eyes are located, measuring the intensity changes in the eye area determine the eyes are open or closed. If the eyes are found closed for 4 consecutive frames, it is confirm.