Driver fatigue detection using a genetic algorithm

Abstract

Nowadays, many traffic accidents occur due to driver fatigue. Driver fatigue detection based on computer vision is one of the most hopeful applications of image recognition technology. There are several factors that reflect driver's fatigue. Many efforts have been made to develop fatigue monitoring, but most of them focus on only a single behavior, a feature of the eyes, or a head motion, or mouth motion, etc. When fatigue monitoring is implemented on a real model, it is difficult to predict the driver fatigue accurately or reliably based only on a single driver behavior. Additionally, the changes in a driver's performance are more complicated and not reliable. In this article, we represent a model that simulates a space in a real car. A web camera as a vision sensor is located to acquire video-images of the driver. Three typical characteristics of driver fatigue are involved, pupil shape, eye blinking frequency, and yawn frequency. As the influences of these characteristics on driver fatigue are quite different from each other, we propose a genetic algorithm (GA)-based neural network (NN) system to fuse these three parameters. We use the GA to determine the structure of the neural network system. Finally, simulation results show that the proposed fatigue monitoring system detects driver fatigue probability more exactly and robustly.