Author: Yi Shen
Shen, Yi, 2017 Drowsiness Detection and Analysis, Flinders University, School of Computer Science, Engineering and Mathematics
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Driver fatigue is one of major reasons which causes vehicle accidents. The drivers’ drowsiness detection can be described by ocular measures, such as eye blink duration and blink frequency. The aim of this project is to use ocular measures to detect and analyse drowsiness in real-time. This project consists of four parts which are eye detection, eye tracking, eye blink detection and drowsiness detection respectively. Eye detection used the Viola-Jones algorithm which is based on cascade Haar classifiers to identify region of interest from images. Eye tracking combined the Camshift algorithm with KLT algorithm to track eye pair of participants in the real-time detection. Eye blink detection used the correlation coefficient algorithm to calculate correlation scores between the open eyes template image and the region of interest of every frame. The analysis of eye blink detection measured the blink duration, blink frequency in drowsy, alert, normal state. Also, it compared the differences of blink duration and frequencies in different drowsiness states. Furthermore, it analysed factors which affect the accuracy of drowsiness detection.
Keywords: Drowsiness detection, Classifier, Camshift, KLT, Blink frequency, Blink duration.
Subject: Engineering thesis
Thesis type: Masters
Completed: 2017
School: School of Computer Science, Engineering and Mathematics
Supervisor: Sherry Randhawa