{"title":"驾驶员困倦与疲劳检测的文献综述","authors":"Hamed Laouz, Soheyb Ayad, L. Terrissa","doi":"10.1109/ISCV49265.2020.9204306","DOIUrl":null,"url":null,"abstract":"Traffic accidents always cause great material and human losses. One of the most important causes of these accidents is the human factor, which is usually caused by fatigue or drowsiness. To address this problem, several approaches were proposed to predict the driver state. Some solutions are based on the measurement of the driver behavior such as: the head movement, the duration of the blink of the eye, the observation of the mouth expression. … etc., while the others are based on the measurements of the physiological signals to get information about the internal state of the driver’s body. These measurements are collected using different sensors such as Electrocardiogram (ECG), Electromyography (EMG), Electroencephalography (EEG), and Electrooculogram (EOG). In this paper, we presented a literature review on the recent related works in this field. In addition, we compared the methods used in each measurement approach. Finally, a detailed discussion according to the methods efficiency as well as the achieved results will be given.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Literature Review on Driver’s Drowsiness and Fatigue Detection\",\"authors\":\"Hamed Laouz, Soheyb Ayad, L. Terrissa\",\"doi\":\"10.1109/ISCV49265.2020.9204306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic accidents always cause great material and human losses. One of the most important causes of these accidents is the human factor, which is usually caused by fatigue or drowsiness. To address this problem, several approaches were proposed to predict the driver state. Some solutions are based on the measurement of the driver behavior such as: the head movement, the duration of the blink of the eye, the observation of the mouth expression. … etc., while the others are based on the measurements of the physiological signals to get information about the internal state of the driver’s body. These measurements are collected using different sensors such as Electrocardiogram (ECG), Electromyography (EMG), Electroencephalography (EEG), and Electrooculogram (EOG). In this paper, we presented a literature review on the recent related works in this field. In addition, we compared the methods used in each measurement approach. Finally, a detailed discussion according to the methods efficiency as well as the achieved results will be given.\",\"PeriodicalId\":313743,\"journal\":{\"name\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCV49265.2020.9204306\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCV49265.2020.9204306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Literature Review on Driver’s Drowsiness and Fatigue Detection
Traffic accidents always cause great material and human losses. One of the most important causes of these accidents is the human factor, which is usually caused by fatigue or drowsiness. To address this problem, several approaches were proposed to predict the driver state. Some solutions are based on the measurement of the driver behavior such as: the head movement, the duration of the blink of the eye, the observation of the mouth expression. … etc., while the others are based on the measurements of the physiological signals to get information about the internal state of the driver’s body. These measurements are collected using different sensors such as Electrocardiogram (ECG), Electromyography (EMG), Electroencephalography (EEG), and Electrooculogram (EOG). In this paper, we presented a literature review on the recent related works in this field. In addition, we compared the methods used in each measurement approach. Finally, a detailed discussion according to the methods efficiency as well as the achieved results will be given.