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Real-time system for monitoring driver vigilance
This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visua...
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Published in: | IEEE transactions on intelligent transportation systems 2006-03, Vol.7 (1), p.63-77 |
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creator | Bergasa, L.M. Nuevo, J. Sotelo, M.A. Barea, R. Lopez, M.E. |
description | This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented |
doi_str_mv | 10.1109/TITS.2006.869598 |
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It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. 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Computational geometry ; percent eye closure (PERCLOS) ; Pulse measurements ; Real time ; Real time systems ; Road accidents ; Robotics ; Statistics ; Studies ; US Department of Transportation ; Vehicle crash testing ; Vehicle driving ; Vigilance ; Vision systems ; Visual ; visual fatigue behaviors</subject><ispartof>IEEE transactions on intelligent transportation systems, 2006-03, Vol.7 (1), p.63-77</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. 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Computational geometry</subject><subject>percent eye closure (PERCLOS)</subject><subject>Pulse measurements</subject><subject>Real time</subject><subject>Real time systems</subject><subject>Road accidents</subject><subject>Robotics</subject><subject>Statistics</subject><subject>Studies</subject><subject>US Department of Transportation</subject><subject>Vehicle crash testing</subject><subject>Vehicle driving</subject><subject>Vigilance</subject><subject>Vision systems</subject><subject>Visual</subject><subject>visual fatigue behaviors</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNqFkU1Lw0AQhoMoWKt3wUsQ1FPqbPb7KMWPQkHQel42m03ZkjR1Ny3037shhYIHncsMO8_MzsybJNcIJgiBfFzMFp-THIBNBJNUipNkhCgVGQBip32ck0wChfPkIoRVfCUUoVECH1bXWecam4Z96GyTVq1Pm3btuta79TItvdtZn-7c0tV6bexlclbpOtirgx8nXy_Pi-lbNn9_nU2f5pkhiHQZNbYAq8EwonlJuS40prKweUGqEmOoOGVUxAF1zkxVGZ4zTUQJWAumUVHgcfIw9N349ntrQ6caF4yt4xC23QYlJMsx4VRG8v5PMpeAZLT_QQFcUEkjePsLXLVbv47rKsEYJiLnECEYIOPbELyt1Ma7Rvu9QqB6SVQvieolUYMkseTu0FcHo-vKx4O6cKzjNP4ueORuBs5Za49pBphSjH8AJIKTPQ</recordid><startdate>20060301</startdate><enddate>20060301</enddate><creator>Bergasa, L.M.</creator><creator>Nuevo, J.</creator><creator>Sotelo, M.A.</creator><creator>Barea, R.</creator><creator>Lopez, M.E.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Systems</topic><topic>Device driver programs</topic><topic>Driver vigilance</topic><topic>Exact sciences and technology</topic><topic>eyelid movement</topic><topic>face position</topic><topic>Fatigue</topic><topic>Frequency</topic><topic>Fuzzy</topic><topic>fuzzy classifier</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>Monitoring</topic><topic>Pattern recognition. Digital image processing. 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It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TITS.2006.869598</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Applied sciences Artificial intelligence Computer programs Computer science control theory systems Computerized monitoring Control theory. Systems Device driver programs Driver vigilance Exact sciences and technology eyelid movement face position Fatigue Frequency Fuzzy fuzzy classifier Ground, air and sea transportation, marine construction Monitoring Pattern recognition. Digital image processing. Computational geometry percent eye closure (PERCLOS) Pulse measurements Real time Real time systems Road accidents Robotics Statistics Studies US Department of Transportation Vehicle crash testing Vehicle driving Vigilance Vision systems Visual visual fatigue behaviors |
title | Real-time system for monitoring driver vigilance |
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