research-article

Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and support vector machines

Authors:

Soonwook Chung,

Hyeran ByunAuthors Info & Claims

ICUIMC '14: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

Article No.: 92, Pages 1 - 5

https://doi.org/10.1145/2557977.2558090

Published: 09 January 2014 Publication History

ICUIMC '14: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and support vector machines

Pages 1 - 5

Abstract
References

Abstract

In this paper, we propose a robust illumination system for speed-limit sign recognition in real-time. Real-time traffic sign detection with various illuminations is one of the challenges in a vision-based intelligent vehicle system, as illumination varies greatly in real-world road images based on factors such as driving time, weather, lighting conditions, and driving directions. Our method uses a MCT (Modified Census Transform) as an illumination-invariant method for the real-time detection of traffic signs and uses a SVM (Support Vector Machine) as a classifier for detection and validation. With the proposed method, we have obtained a very high detection rate of 99.8% and recognition rates of 98.4% on various real-world driving images.

References

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Digital Library

Cited By

Seraj MRosales-Castellanos AShalkamy AEl-Basyouny KQiu T(2021)The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition PerformanceJournal of Advanced Transportation10.1155/2021/55135522021(1-15)Online publication date: 11-Aug-2021
https://doi.org/10.1155/2021/5513552
Chutian D(2020)A Review on the Extraction of Region of Interest in Traffic Sign Recognition System2020 International Conference on Computing and Data Science (CDS)10.1109/CDS49703.2020.00010(19-22)Online publication date: Aug-2020
https://doi.org/10.1109/CDS49703.2020.00010
Saadna YBehloul AMezzoudj S(2019)Speed limit sign detection and recognition system using SVM and MNIST datasetsNeural Computing and Applications10.1007/s00521-018-03994-w31:9(5005-5015)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s00521-018-03994-w
Show More Cited By

Index Terms

Real-time illumination-invariant speed-limit sign recognition based on a modified census transform and support vector machines
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Conferences

ICUIMC '14: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication

January 2014

757 pages

ISBN:9781450326445

DOI:10.1145/2557977

Conference Chairs:
Dongsoo S. Kim
Indiana University
,
Sang-Wook Kim
Hanyang University, Korea
,
General Chairs:
Suk-Han Lee
Sungkyunkwan University
,
Korea Lajos Hanzo
University of Southampton, UK
,
Roslan Ismail
Universiti Kuala Lumpur, Malaysia

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing
SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 January 2014

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Funding Sources

Ministry of Education, Science and Technology

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ICUIMC '14

Sponsor:

SIGAPP
SKKU

ICUIMC '14: The 8th International Conference on Ubiquitous Information Management and Communication

January 9 - 11, 2014

Siem Reap, Cambodia

Acceptance Rates

ICUIMC '14 Paper Acceptance Rate 116 of 407 submissions, 29%;

Overall Acceptance Rate 251 of 941 submissions, 27%

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Citations

Cited By

Seraj MRosales-Castellanos AShalkamy AEl-Basyouny KQiu T(2021)The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition PerformanceJournal of Advanced Transportation10.1155/2021/55135522021(1-15)Online publication date: 11-Aug-2021
https://doi.org/10.1155/2021/5513552
Chutian D(2020)A Review on the Extraction of Region of Interest in Traffic Sign Recognition System2020 International Conference on Computing and Data Science (CDS)10.1109/CDS49703.2020.00010(19-22)Online publication date: Aug-2020
https://doi.org/10.1109/CDS49703.2020.00010
Saadna YBehloul AMezzoudj S(2019)Speed limit sign detection and recognition system using SVM and MNIST datasetsNeural Computing and Applications10.1007/s00521-018-03994-w31:9(5005-5015)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s00521-018-03994-w
Li YMogelmose ATrivedi M(2016)Pushing the “Speed Limit”: High-Accuracy US Traffic Sign Recognition With Convolutional Neural NetworksIEEE Transactions on Intelligent Vehicles10.1109/TIV.2016.26155231:2(167-176)Online publication date: Jun-2016
https://doi.org/10.1109/TIV.2016.2615523

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