Vigilance Praepostor & Consciousness Analyzer

Abstract

This thesis is focused on driver vigilance detection and the objective of this thesis is to recognize driver’s state with high performance. Drowsy driving is one of the main reasons of traffic accidents in which many people die or get injured. Driver vigilance detection is based on method focusing on driver’s state. Furthermore, methods focusing on driver’s state are divided into two groups: methods using physiological signals and methods using computer vision. In this thesis, driver data are pictures captured by a camera and the method proposed belongs to the group that uses computer vision to detect driver’s state. There are two main states of a driver, those are vigilant and drowsy states. Pictures captured are analyzed by making use of image processing techniques. Eye regions are detected, and those eye regions are input to right and left eye region classifiers, which are implemented using artificial neural networks. The neural networks classify the right and left eye as open or closed eye and yawning state. The eye and yawning states along the pictures are fused and the driver’s state is predicted as vigilant or drowsy. The proposed method is tested on pictures. The accuracy of the driver’s state recognition method is 99.1% and the accuracy of our eye state recognition method is 94%. Those results are comparable with the results in literature.