OcrV1, PascalFrancis, Corpus, bibRecord, 000167

ESARR: Enhanced Situational Awareness via Road Sign Recognition

Identifieur interne : 000167 ( PascalFrancis/Corpus ); précédent : 000166; suivant : 000168

ESARR: Enhanced Situational Awareness via Road Sign Recognition

Auteurs : V. E. Perlin ; D. B. Johnson ; M. M. Rohde ; R. M. Lupa ; G. Fiorani ; S. Mohammad

Source :

Proceedings of SPIE, the International Society for Optical Engineering [ 0277-786X ] ; 2010.

RBID : Pascal:10-0425678

Descripteurs français

Pascal (Inist)
- Traitement image, Système temps réel, Imagerie, Traitement automatique, Route, Mesure position, Reconnaissance forme, Taux fausse alarme, Système information géographique, Localisation, Temps réel, 0130C, 0707, 4230V.

English descriptors

KwdEn :
- Automatic processing, False alarm rate, Geographic information systems, Image processing, Imagery, Localization, Pattern recognition, Position measurement, Real time, Real time systems, Roads.

Abstract

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A02	`01`			`@0 PSISDG`
A03		`1`		`@0 Proc. SPIE Int. Soc. Opt. Eng.`
A05				`@2 7692`
A08	`01`	`1`	`ENG`	`@1 ESARR: Enhanced Situational Awareness via Road Sign Recognition`
A09	`01`	`1`	`ENG`	`@1 Unmanned systems technology XII : 6-9 April 2010, Orlando, Florida, United States`
A11	`01`	`1`		`@1 PERLIN (V. E.)`
A11	`02`	`1`		`@1 JOHNSON (D. B.)`
A11	`03`	`1`		`@1 ROHDE (M. M.)`
A11	`04`	`1`		`@1 LUPA (R. M.)`
A11	`05`	`1`		`@1 FIORANI (G.)`
A11	`06`	`1`		`@1 MOHAMMAD (S.)`
A12	`01`	`1`		`@1 GERHART (Grant R.) @9 ed.`
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C01	`01`		`ENG`	@0 The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.
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C03	`04`	`X`	`ENG`	`@0 Automatic processing @5 61`
C03	`04`	`X`	`SPA`	`@0 Tratamiento automático @5 61`
C03	`05`	`3`	`FRE`	`@0 Route @5 62`
C03	`05`	`3`	`ENG`	`@0 Roads @5 62`
C03	`06`	`3`	`FRE`	`@0 Mesure position @5 63`
C03	`06`	`3`	`ENG`	`@0 Position measurement @5 63`
C03	`07`	`3`	`FRE`	`@0 Reconnaissance forme @5 64`
C03	`07`	`3`	`ENG`	`@0 Pattern recognition @5 64`
C03	`08`	`X`	`FRE`	`@0 Taux fausse alarme @5 65`
C03	`08`	`X`	`ENG`	`@0 False alarm rate @5 65`
C03	`08`	`X`	`SPA`	`@0 Porcentaje falsa alarma @5 65`
C03	`09`	`3`	`FRE`	`@0 Système information géographique @5 66`
C03	`09`	`3`	`ENG`	`@0 Geographic information systems @5 66`
C03	`10`	`X`	`FRE`	`@0 Localisation @5 67`
C03	`10`	`X`	`ENG`	`@0 Localization @5 67`
C03	`10`	`X`	`SPA`	`@0 Localización @5 67`
C03	`11`	`X`	`FRE`	`@0 Temps réel @5 68`
C03	`11`	`X`	`ENG`	`@0 Real time @5 68`
C03	`11`	`X`	`SPA`	`@0 Tiempo real @5 68`
C03	`12`	`3`	`FRE`	`@0 0130C @4 INC @5 83`
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A30	`01`	`1`	`ENG`	`@1 Unmanned systems technology @2 12 @3 Orlando FL USA @4 2010`

Format Inist (serveur)

NO :	PASCAL 10-0425678 INIST
ET :	ESARR: Enhanced Situational Awareness via Road Sign Recognition
AU :	PERLIN (V. E.); JOHNSON (D. B.); ROHDE (M. M.); LUPA (R. M.); FIORANI (G.); MOHAMMAD (S.); GERHART (Grant R.); GAGE (Douglas Whitney); SHOEMAKER (Chuck M.)
AF :	Quantum Signal, LLC, 3741 Plaza Drive/Ann Arbor, MI 48108/Etats-Unis (1 aut., 2 aut., 3 aut., 4 aut.); U.S. Army Tank-Automotive Research, Development and Engineering Center, 6501 E. 11 Mile Rd./Warren, MI, 48397/Etats-Unis (5 aut., 6 aut.)
DT :	Publication en série; Congrès; Niveau analytique
SO :	Proceedings of SPIE, the International Society for Optical Engineering; ISSN 0277-786X; Coden PSISDG; Etats-Unis; Da. 2010; Vol. 7692; 76920G.1-76920G.13; Bibl. 16 ref.
LA :	Anglais
EA :	The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.
CC :	001B00A30C; 001B00G07; 001B40B30V
FD :	Traitement image; Système temps réel; Imagerie; Traitement automatique; Route; Mesure position; Reconnaissance forme; Taux fausse alarme; Système information géographique; Localisation; Temps réel; 0130C; 0707; 4230V
ED :	Image processing; Real time systems; Imagery; Automatic processing; Roads; Position measurement; Pattern recognition; False alarm rate; Geographic information systems; Localization; Real time
SD :	Imaginería; Tratamiento automático; Porcentaje falsa alarma; Localización; Tiempo real
LO :	INIST-21760.354000174694620150
ID :	10-0425678

Links to Exploration step

Pascal:10-0425678

Le document en format XML

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<front><div type="abstract" xml:lang="en">The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.</div>
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<fC01 i1="01" l="ENG"><s0>The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.</s0>
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<s5>84</s5>
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<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
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<s2>12</s2>
<s3>Orlando FL USA</s3>
<s4>2010</s4>
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<server><NO>PASCAL 10-0425678 INIST</NO>
<ET>ESARR: Enhanced Situational Awareness via Road Sign Recognition</ET>
<AU>PERLIN (V. E.); JOHNSON (D. B.); ROHDE (M. M.); LUPA (R. M.); FIORANI (G.); MOHAMMAD (S.); GERHART (Grant R.); GAGE (Douglas Whitney); SHOEMAKER (Chuck M.)</AU>
<AF>Quantum Signal, LLC, 3741 Plaza Drive/Ann Arbor, MI 48108/Etats-Unis (1 aut., 2 aut., 3 aut., 4 aut.); U.S. Army Tank-Automotive Research, Development and Engineering Center, 6501 E. 11 Mile Rd./Warren, MI, 48397/Etats-Unis (5 aut., 6 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
<SO>Proceedings of SPIE, the International Society for Optical Engineering; ISSN 0277-786X; Coden PSISDG; Etats-Unis; Da. 2010; Vol. 7692; 76920G.1-76920G.13; Bibl. 16 ref.</SO>
<LA>Anglais</LA>
<EA>The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). Sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.</EA>
<CC>001B00A30C; 001B00G07; 001B40B30V</CC>
<FD>Traitement image; Système temps réel; Imagerie; Traitement automatique; Route; Mesure position; Reconnaissance forme; Taux fausse alarme; Système information géographique; Localisation; Temps réel; 0130C; 0707; 4230V</FD>
<ED>Image processing; Real time systems; Imagery; Automatic processing; Roads; Position measurement; Pattern recognition; False alarm rate; Geographic information systems; Localization; Real time</ED>
<SD>Imaginería; Tratamiento automático; Porcentaje falsa alarma; Localización; Tiempo real</SD>
<LO>INIST-21760.354000174694620150</LO>
<ID>10-0425678</ID>
</server>
</inist>
</record>

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ESARR: Enhanced Situational Awareness via Road Sign Recognition

ESARR: Enhanced Situational Awareness via Road Sign Recognition

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