Bruno Eraldo, G. Quispe, Heyul Chavez-Arias, C. Raymundo-Ibañez, Francisco Dominguez
{"title":"设计了一种通过图像处理来减少因困倦引起的交通事故的控制与监控系统","authors":"Bruno Eraldo, G. Quispe, Heyul Chavez-Arias, C. Raymundo-Ibañez, Francisco Dominguez","doi":"10.1109/CONCAPANXXXIX47272.2019.8976928","DOIUrl":null,"url":null,"abstract":"It is known that 33% of traffic accidents worldwide are caused by drunk driving or drowsiness [1] [2], so a drowsiness level detection system that integrates image processing was developed with the use of Raspberry Pi3 with the OpenCV library; and sensors such as MQ-3 that measures the percentage of alcohol and the S9 sensor that measures the heart rate. In addition, it has an alert system and as an interface for the visualization of the data measured by the sensors a touch screen. With the image processing technique, facial expressions are analyzed, while physiological behaviors such as heart rate and alcohol percentage are measured with the sensors. In image test training you get an accuracy of x in a response time of x seconds. On the other hand, the evaluation of the operation of the sensors in 90% effective. So the method developed is effective and feasible.","PeriodicalId":272652,"journal":{"name":"2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing\",\"authors\":\"Bruno Eraldo, G. Quispe, Heyul Chavez-Arias, C. Raymundo-Ibañez, Francisco Dominguez\",\"doi\":\"10.1109/CONCAPANXXXIX47272.2019.8976928\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is known that 33% of traffic accidents worldwide are caused by drunk driving or drowsiness [1] [2], so a drowsiness level detection system that integrates image processing was developed with the use of Raspberry Pi3 with the OpenCV library; and sensors such as MQ-3 that measures the percentage of alcohol and the S9 sensor that measures the heart rate. In addition, it has an alert system and as an interface for the visualization of the data measured by the sensors a touch screen. With the image processing technique, facial expressions are analyzed, while physiological behaviors such as heart rate and alcohol percentage are measured with the sensors. In image test training you get an accuracy of x in a response time of x seconds. On the other hand, the evaluation of the operation of the sensors in 90% effective. So the method developed is effective and feasible.\",\"PeriodicalId\":272652,\"journal\":{\"name\":\"2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CONCAPANXXXIX47272.2019.8976928\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONCAPANXXXIX47272.2019.8976928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of a control and monitoring system to reduce traffic accidents due to drowsiness through image processing
It is known that 33% of traffic accidents worldwide are caused by drunk driving or drowsiness [1] [2], so a drowsiness level detection system that integrates image processing was developed with the use of Raspberry Pi3 with the OpenCV library; and sensors such as MQ-3 that measures the percentage of alcohol and the S9 sensor that measures the heart rate. In addition, it has an alert system and as an interface for the visualization of the data measured by the sensors a touch screen. With the image processing technique, facial expressions are analyzed, while physiological behaviors such as heart rate and alcohol percentage are measured with the sensors. In image test training you get an accuracy of x in a response time of x seconds. On the other hand, the evaluation of the operation of the sensors in 90% effective. So the method developed is effective and feasible.
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