K. J. Raman, A. Azman, Venosha Arumugam, S. Z. Ibrahim, S. Yogarayan, Mohd Fikri Azli Abdullah, Siti Fatimah Abdul Razak, A. H. Muhamad Amin, Kalaiarasi Sonaimuthu
{"title":"基于打哈欠和头部运动的疲劳监测","authors":"K. J. Raman, A. Azman, Venosha Arumugam, S. Z. Ibrahim, S. Yogarayan, Mohd Fikri Azli Abdullah, Siti Fatimah Abdul Razak, A. H. Muhamad Amin, Kalaiarasi Sonaimuthu","doi":"10.1109/ICOICT.2018.8528759","DOIUrl":null,"url":null,"abstract":"Driver fatigue is one of the main reasons causing traffic accidents. Yawning is an important character of driver fatigue. Mouth geometric character represents state of driver. So driver's mouth detection and information extraction is especially important. This paper proposes to locate and track driver's yawning based on mouth using camera to detect driver's fatigue in driving environment. Then contour algorithm features to detect driver's yawning and track it according to historical position. At last, yawning is detected by the ratio of mouth in the name of dark region as when he mouth is widely open. Through this method the resolution ratios which are transferred to percentage of driver's mouth is higher than one camera and the feature information to be more accuracy. The head movement is partially a phase where the fatigue would be detected by using face detection itself to detect the head movement. In order to get a clear vision of driver's fatigue, yawning and head movement activity will be the best to detect in a driving environment since it provides a better basis for driver fatigue judging.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Fatigue Monitoring Based on Yawning and Head Movement\",\"authors\":\"K. J. Raman, A. Azman, Venosha Arumugam, S. Z. Ibrahim, S. Yogarayan, Mohd Fikri Azli Abdullah, Siti Fatimah Abdul Razak, A. H. Muhamad Amin, Kalaiarasi Sonaimuthu\",\"doi\":\"10.1109/ICOICT.2018.8528759\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Driver fatigue is one of the main reasons causing traffic accidents. Yawning is an important character of driver fatigue. Mouth geometric character represents state of driver. So driver's mouth detection and information extraction is especially important. This paper proposes to locate and track driver's yawning based on mouth using camera to detect driver's fatigue in driving environment. Then contour algorithm features to detect driver's yawning and track it according to historical position. At last, yawning is detected by the ratio of mouth in the name of dark region as when he mouth is widely open. Through this method the resolution ratios which are transferred to percentage of driver's mouth is higher than one camera and the feature information to be more accuracy. The head movement is partially a phase where the fatigue would be detected by using face detection itself to detect the head movement. In order to get a clear vision of driver's fatigue, yawning and head movement activity will be the best to detect in a driving environment since it provides a better basis for driver fatigue judging.\",\"PeriodicalId\":266335,\"journal\":{\"name\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOICT.2018.8528759\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Information and Communication Technology (ICoICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOICT.2018.8528759","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fatigue Monitoring Based on Yawning and Head Movement
Driver fatigue is one of the main reasons causing traffic accidents. Yawning is an important character of driver fatigue. Mouth geometric character represents state of driver. So driver's mouth detection and information extraction is especially important. This paper proposes to locate and track driver's yawning based on mouth using camera to detect driver's fatigue in driving environment. Then contour algorithm features to detect driver's yawning and track it according to historical position. At last, yawning is detected by the ratio of mouth in the name of dark region as when he mouth is widely open. Through this method the resolution ratios which are transferred to percentage of driver's mouth is higher than one camera and the feature information to be more accuracy. The head movement is partially a phase where the fatigue would be detected by using face detection itself to detect the head movement. In order to get a clear vision of driver's fatigue, yawning and head movement activity will be the best to detect in a driving environment since it provides a better basis for driver fatigue judging.