计算机工程与应用Issue(17)：199-203,5.DOI:10.3778/j.issn.1002-8331.1111-0503
眼睛疲劳程度判定方法研究
Study on method of determining eye fatigue degree
摘要
Abstract
when employing the machine vision system for monitoring the driver’s eyes fatigue state, due to image acquisition and image processing takes some time, reducing the system’s sampling rate, and the blink process is very short under normal circumstances, low sampling rate will loss the eyes of some key state information. This article directly studies the video image sequences, and uses the statistical methods to determine the maximum of open eye, then applies the PERCLOS method to determine the extent of eye fatigue. Study the varied sample intervals to impact on the PERCLOS value by sampling the video image sequences. Through six testers’data show that, when the PERCLOS value reaches 15%, the sign of fatigue is obvious, with the deepening of fatigue, the PERCLOS value increases, adjusting the sample interval, it is found that when sample interval between the 40~120 ms, the PERCLOS value is relatively stable, the maximum relative error is 20.17%, but when the sample interval is greater than 120 ms, the PERCLOS fluctuation is high, the maximum relative error is 54.07%, which will affect the fatigue judgement.关键词
PERCLOS/眼睛睁开程度/疲劳判定Key words
PERCLOS/eyes open degree/determining fatigue分类
信息技术与安全科学引用本文复制引用
苑玮琦,滕红艳..眼睛疲劳程度判定方法研究[J].计算机工程与应用,2013,(17):199-203,5.基金项目
国家自然科学基金（No.60672078）。 （）
