OcrV1, Main, Exploration, bibRecord, 000785

ESARR: Enhanced Situational Awareness via Road Sign Recognition

Identifieur interne : 000785 ( Main/Exploration ); précédent : 000784; suivant : 000786

ESARR: Enhanced Situational Awareness via Road Sign Recognition

Auteurs : V. E. Perlin [États-Unis] ; D. B. Johnson [États-Unis] ; M. M. Rohde [États-Unis] ; R. M. Lupa [États-Unis] ; G. Fiorani [États-Unis] ; S. Mohammad [États-Unis]

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.

Affiliations:

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: 000167
to stream PascalFrancis, to step Curation: 000610
to stream PascalFrancis, to step Checkpoint: 000159
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to stream Main, to step Curation: 000785

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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ESARR: Enhanced Situational Awareness via Road Sign Recognition

ESARR: Enhanced Situational Awareness via Road Sign Recognition

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri