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IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents
At present time, drowsy driving has become one of the major issues of the traffic collision. According to statistics, a large number of road accidents occur due to drowsy driving which results in severe injuries and deaths. For this reason, various studies were done in designing systems that can exa...
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creator | Hossain, Md. Yousuf George, Fabian Parsia |
description | At present time, drowsy driving has become one of the major issues of the traffic collision. According to statistics, a large number of road accidents occur due to drowsy driving which results in severe injuries and deaths. For this reason, various studies were done in designing systems that can examine the driver fatigue and alert him beforehand, thus preventing him to fall asleep behind the wheel and cause an accident. Some traditional approaches used vehicle-based measures to design their system, however, such measurements are highly influenced by the structure of the road, type of vehicle and the driving skill. Other approaches used psychological measures for their system that tend to provide better accuracy in monitoring the drowsiness of the driver. However, such techniques are usually intrusive as electrodes are required to be placed on the head and body. Furthermore, there are few existing researches in which subjective measurements are used as the input for the system, but, such methods can distract the driver and lead to an ambiguous result. In this paper, we proposed a system that is absolutely nonintrusive and real-time. Our proposed system used the eye closure ratio as input parameter to detect the drowsiness of the driver. If the eye closure ratio deteriorates from the standard ratio, the driver is alerted with the help of a buzzer. For our system, a Pi camera is used to capture the images of the driver's eye and the entire system is incorporated using Raspberry-Pi. |
doi_str_mv | 10.1109/ICIIBMS.2018.8550026 |
format | conference_proceeding |
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However, such techniques are usually intrusive as electrodes are required to be placed on the head and body. Furthermore, there are few existing researches in which subjective measurements are used as the input for the system, but, such methods can distract the driver and lead to an ambiguous result. In this paper, we proposed a system that is absolutely nonintrusive and real-time. Our proposed system used the eye closure ratio as input parameter to detect the drowsiness of the driver. If the eye closure ratio deteriorates from the standard ratio, the driver is alerted with the help of a buzzer. For our system, a Pi camera is used to capture the images of the driver's eye and the entire system is incorporated using Raspberry-Pi.</description><identifier>EISSN: 2189-8723</identifier><identifier>EISBN: 9781538675168</identifier><identifier>EISBN: 1538675161</identifier><identifier>DOI: 10.1109/ICIIBMS.2018.8550026</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cameras ; Computer science ; Computer Vision ; Drowsy Driving ; Ear ; Eye Aspect Ratio ; Face ; Facial Landmark ; Feature extraction ; Pi Camera Module ; Raspberry Pi ; Training ; Vehicles</subject><ispartof>2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2018, Vol.3, p.190-195</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c226t-a0a8746693ce9caaf0b4d022371c459551282a1bea129322c95ac5a2768ce20f3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8550026$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8550026$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hossain, Md. Yousuf</creatorcontrib><creatorcontrib>George, Fabian Parsia</creatorcontrib><title>IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents</title><title>2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)</title><addtitle>ICIIBMS</addtitle><description>At present time, drowsy driving has become one of the major issues of the traffic collision. According to statistics, a large number of road accidents occur due to drowsy driving which results in severe injuries and deaths. For this reason, various studies were done in designing systems that can examine the driver fatigue and alert him beforehand, thus preventing him to fall asleep behind the wheel and cause an accident. Some traditional approaches used vehicle-based measures to design their system, however, such measurements are highly influenced by the structure of the road, type of vehicle and the driving skill. Other approaches used psychological measures for their system that tend to provide better accuracy in monitoring the drowsiness of the driver. However, such techniques are usually intrusive as electrodes are required to be placed on the head and body. Furthermore, there are few existing researches in which subjective measurements are used as the input for the system, but, such methods can distract the driver and lead to an ambiguous result. In this paper, we proposed a system that is absolutely nonintrusive and real-time. Our proposed system used the eye closure ratio as input parameter to detect the drowsiness of the driver. If the eye closure ratio deteriorates from the standard ratio, the driver is alerted with the help of a buzzer. For our system, a Pi camera is used to capture the images of the driver's eye and the entire system is incorporated using Raspberry-Pi.</description><subject>Cameras</subject><subject>Computer science</subject><subject>Computer Vision</subject><subject>Drowsy Driving</subject><subject>Ear</subject><subject>Eye Aspect Ratio</subject><subject>Face</subject><subject>Facial Landmark</subject><subject>Feature extraction</subject><subject>Pi Camera Module</subject><subject>Raspberry Pi</subject><subject>Training</subject><subject>Vehicles</subject><issn>2189-8723</issn><isbn>9781538675168</isbn><isbn>1538675161</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1KAzEURqMgWGqfQBd5gak3N5NMsuyPPwOVSlvBXbnN3NFI25HJUOnbW7SrA9-Bb3GEuFMwVAr8fTkpy_HLcoig3NAZA4D2Qgx84ZTRzhZGWXcpeqicz1yB-loMUvoCAI2Qo8WeeC_nKzmmxJVcMG2zVdyxnLbNTzqeEA9x_yGn3HHoYrOXy2PqeCfrppXdJ8vXlg-8_zNNLRcNVXIUQqxOW7oRVzVtEw_O7Iu3x4fV5DmbzZ_KyWiWBUTbZQTkitxarwP7QFTDJq8AURcq5MYbo9AhqQ2TQq8RgzcUDGFhXWCEWvfF7f9vZOb1dxt31B7X5xb6FxGAUic</recordid><startdate>201810</startdate><enddate>201810</enddate><creator>Hossain, Md. Yousuf</creator><creator>George, Fabian Parsia</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201810</creationdate><title>IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents</title><author>Hossain, Md. Yousuf ; George, Fabian Parsia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c226t-a0a8746693ce9caaf0b4d022371c459551282a1bea129322c95ac5a2768ce20f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Cameras</topic><topic>Computer science</topic><topic>Computer Vision</topic><topic>Drowsy Driving</topic><topic>Ear</topic><topic>Eye Aspect Ratio</topic><topic>Face</topic><topic>Facial Landmark</topic><topic>Feature extraction</topic><topic>Pi Camera Module</topic><topic>Raspberry Pi</topic><topic>Training</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Hossain, Md. Yousuf</creatorcontrib><creatorcontrib>George, Fabian Parsia</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hossain, Md. Yousuf</au><au>George, Fabian Parsia</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents</atitle><btitle>2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)</btitle><stitle>ICIIBMS</stitle><date>2018-10</date><risdate>2018</risdate><volume>3</volume><spage>190</spage><epage>195</epage><pages>190-195</pages><eissn>2189-8723</eissn><eisbn>9781538675168</eisbn><eisbn>1538675161</eisbn><abstract>At present time, drowsy driving has become one of the major issues of the traffic collision. According to statistics, a large number of road accidents occur due to drowsy driving which results in severe injuries and deaths. For this reason, various studies were done in designing systems that can examine the driver fatigue and alert him beforehand, thus preventing him to fall asleep behind the wheel and cause an accident. Some traditional approaches used vehicle-based measures to design their system, however, such measurements are highly influenced by the structure of the road, type of vehicle and the driving skill. Other approaches used psychological measures for their system that tend to provide better accuracy in monitoring the drowsiness of the driver. However, such techniques are usually intrusive as electrodes are required to be placed on the head and body. Furthermore, there are few existing researches in which subjective measurements are used as the input for the system, but, such methods can distract the driver and lead to an ambiguous result. In this paper, we proposed a system that is absolutely nonintrusive and real-time. Our proposed system used the eye closure ratio as input parameter to detect the drowsiness of the driver. If the eye closure ratio deteriorates from the standard ratio, the driver is alerted with the help of a buzzer. For our system, a Pi camera is used to capture the images of the driver's eye and the entire system is incorporated using Raspberry-Pi.</abstract><pub>IEEE</pub><doi>10.1109/ICIIBMS.2018.8550026</doi><tpages>6</tpages></addata></record> |
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identifier | EISSN: 2189-8723 |
ispartof | 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2018, Vol.3, p.190-195 |
issn | 2189-8723 |
language | eng |
recordid | cdi_ieee_primary_8550026 |
source | IEEE Xplore All Conference Series |
subjects | Cameras Computer science Computer Vision Drowsy Driving Ear Eye Aspect Ratio Face Facial Landmark Feature extraction Pi Camera Module Raspberry Pi Training Vehicles |
title | IOT Based Real-Time Drowsy Driving Detection System for the Prevention of Road Accidents |
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