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Traffic-Sign Recognition Systems

  • Book
  • © 2011

Overview

Authors:
  1. Sergio Escalera

    1. Dept. of Applied Mathematics & Analysis, University of Barcelona, Barcelona, Spain

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  2. Xavier Baró

    1. Universitat Oberta de Catalunya, Department of Computer Science, Rambla del Poblenou, Spain

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  3. Oriol Pujol

    1. Dept of Applied Mathematics and Analysis, University of Barcelona, Gran Via de les Corts Catalanes, Spain

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  4. Jordi Vitrià

    1. University of Barcelona, Dept of Applied Mathematics and Analysis, Gran Via de les Corts Catalanes, Spain

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  5. Petia Radeva

    1. University of Barcelona, Dept of Applied Mathematics and Analysis, Gran Via de les Corts Catalanes, Spain

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  • Presents a full generic approach to the detection and recognition of traffic signs, based on state-of-the-art computer vision methods for object detection, and on powerful methods for multiclass classification
  • Surveys a specific methodology for the problem of traffic sign categorization: Error-Correcting Output Codes
  • Includes experimental validation results performed on a mobile mapping application
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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About this book

This work presents a full generic approach to the detection and recognition of traffic signs. The approach 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 research and continuing challenges.

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-vi
    Download chapter PDF

  2. Introduction

    • Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 1-4

  3. Background on Traffic Sign Detection and Recognition

    • Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 5-13

  4. Traffic Sign Detection

    • Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 15-52

  5. Traffic Sign Categorization

    • Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 53-80

  6. Traffic Sign Detection and Recognition System

    • Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 81-94

  7. Conclusion

    • Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva
    Pages 95-96
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Authors and Affiliations

  • Dept. of Applied Mathematics & Analysis, University of Barcelona, Barcelona, Spain

    Sergio Escalera

  • Universitat Oberta de Catalunya, Department of Computer Science, Rambla del Poblenou, Spain

    Xavier Baró

  • Dept of Applied Mathematics and Analysis, University of Barcelona, Gran Via de les Corts Catalanes, Spain

    Oriol Pujol

  • University of Barcelona, Dept of Applied Mathematics and Analysis, Gran Via de les Corts Catalanes, Spain

    Jordi Vitrià, Petia Radeva

Bibliographic Information

  • Book Title: Traffic-Sign Recognition Systems

  • Authors: Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva

  • Series Title: SpringerBriefs in Computer Science

  • DOI: https://doi.org/10.1007/978-1-4471-2245-6

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Sergio Escalera 2011

  • Softcover ISBN: 978-1-4471-2244-9Published: 23 September 2011

  • eBook ISBN: 978-1-4471-2245-6Published: 22 September 2011

  • Series ISSN: 2191-5768

  • Series E-ISSN: 2191-5776

  • Edition Number: 1

  • Number of Pages: VI, 96

  • Number of Illustrations: 34 b/w illustrations

  • Topics: Image Processing and Computer Vision

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