Traffic-Sign Recognition Systems

This work presents a full generic approach to the detection and recognition of traffic signs. The approach, originally developed for a mobile mapping application, is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Escalera, Sergio (Autor), Baró, Xavier (Autor), Pujol, Oriol (Autor), Vitrià, Jordi (Autor), Radeva, Petia (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Springer London : Imprint: Springer, 2011.
Edición:1st ed. 2011.
Colección:SpringerBriefs in Computer Science,
Temas:
Computer vision.
Computer Vision.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Traffic-Sign Recognition Systems  |h [electronic resource] /  |c by Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva. 
250 |a 1st ed. 2011. 
264 1 |a London :  |b Springer London :  |b Imprint: Springer,  |c 2011. 
300 |a VI, 96 p. 34 illus.  |b online resource. 
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505 0 |a Introduction -- Background on Traffic Sign Detection and Recognition -- Traffic Sign Detection -- Traffic Sign Categorization -- Traffic Sign Detection and Recognition System -- Conclusions. 
520 |a This work presents a full generic approach to the detection and recognition of traffic signs. The approach, originally developed for a mobile mapping application, is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization - Error-Correcting Output Codes - and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future lines of research, and continuing challenges for traffic sign recognition. 
650 0 |a Computer vision. 
650 1 4 |a Computer Vision. 
700 1 |a Baró, Xavier.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Pujol, Oriol.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Vitrià, Jordi.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
700 1 |a Radeva, Petia.  |e author.  |4 aut  |4 http://id.loc.gov/vocabulary/relators/aut 
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950 |a Computer Science (R0) (SpringerNature-43710) 

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