Saravanaraj Sathasivam, A. Mahamad, S. Saon, A. Sidek, M. Som, H. A. Ameen
{"title":"Drowsiness Detection System using Eye Aspect Ratio Technique","authors":"Saravanaraj Sathasivam, A. Mahamad, S. Saon, A. Sidek, M. Som, H. A. Ameen","doi":"10.1109/SCOReD50371.2020.9251035","DOIUrl":null,"url":null,"abstract":"Transportation is widely used to allow user travel conveniently from place to place, for a personal of official purpose. Travel during peak hour or holiday, expose the driver to traffic jam for several hour, thus cause the drive to feel drowsy easily due to high concentration and lack of rest. This situation contributes the increasing of the percentage of car incident due to car driver fatigue is the primary origin of the car accident. In this paper, image detection drowsiness system is proposed to detect the state of the car driver using Eye Aspect Ratio (EAR) technique. A developed system that occupied with the Pi camera, Raspberry Pi 4 and GPS module are used to detect and analyse continuously the state of eye closure in real time. This system able to recognize whether the driver is drowsy or not, with the initial, wearing spectacles, dim light and microsleep condition experimental conducted successfully give 90% of accuracy. This situation can increase the vigilant of drivers significantly.","PeriodicalId":142867,"journal":{"name":"2020 IEEE Student Conference on Research and Development (SCOReD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Student Conference on Research and Development (SCOReD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCOReD50371.2020.9251035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
Transportation is widely used to allow user travel conveniently from place to place, for a personal of official purpose. Travel during peak hour or holiday, expose the driver to traffic jam for several hour, thus cause the drive to feel drowsy easily due to high concentration and lack of rest. This situation contributes the increasing of the percentage of car incident due to car driver fatigue is the primary origin of the car accident. In this paper, image detection drowsiness system is proposed to detect the state of the car driver using Eye Aspect Ratio (EAR) technique. A developed system that occupied with the Pi camera, Raspberry Pi 4 and GPS module are used to detect and analyse continuously the state of eye closure in real time. This system able to recognize whether the driver is drowsy or not, with the initial, wearing spectacles, dim light and microsleep condition experimental conducted successfully give 90% of accuracy. This situation can increase the vigilant of drivers significantly.
交通工具被广泛用于方便用户旅行从一个地方到另一个地方,为个人或公务目的。在高峰时间或节假日出行,使驾驶员暴露在交通堵塞中几个小时,从而使驾驶员由于高度集中和缺乏休息而容易感到昏昏欲睡。这种情况促成了汽车事故的百分比的增加,因为汽车驾驶员疲劳是交通事故的主要原因。本文提出了一种利用眼宽高比(EAR)技术检测汽车驾驶员状态的图像检测系统。利用Pi相机、Raspberry Pi 4和GPS模块开发的系统,实时连续检测和分析闭眼状态。该系统能够识别驾驶员是否昏昏欲睡,在初始、戴眼镜、昏暗灯光和微睡眠条件下进行的实验成功地给出了90%的准确率。这种情况可以大大提高司机的警惕性。