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Real-time detection of speed-limit traffic signs on the real road using Haar-like features and boosted cascade

Authors:

Won J. Jeon,

Gustavo Adrian Ruiz Sanchez,

Hyeran ByunAuthors Info & Claims

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

Article No.: 93, Pages 1 - 5

https://doi.org/10.1145/2557977.2558091

Published: 09 January 2014 Publication History

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Abstract

Along with the development of the intelligent vehicle, the Advanced Driver Assistance System(ADAS) has recently become an important issue. Traffic signs on the road provide crucial information to the driver. Recognizing all the traffic signs on the side of the road can be a difficult task for a driver who should watch the road ahead. To solve this problem, this paper proposes real-time detection methods using Haar-like features in a real road driving environment. We implement a reliable reduction method of the search area to improve the detection speed, masking methods and histogram equalization to improve the detection rate. The proposed method has shown higher detection rate and two times faster performance time than previous works.

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Cited By

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Triki NKarray MKsantini M(2023)A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart VehiclesApplied Sciences10.3390/app1308479313:8(4793)Online publication date: 11-Apr-2023
https://doi.org/10.3390/app13084793
Shi LZhou Y(2021)A CNN-based Traffic Sign Detection and Classification Method Using Priori Knowledge2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)10.1109/CONF-SPML54095.2021.00057(258-264)Online publication date: Nov-2021
https://doi.org/10.1109/CONF-SPML54095.2021.00057
Abdi LMeddeb A(2018)Spatially Enhanced Bags of Visual Words Representation to Improve Traffic Signs RecognitionJournal of Signal Processing Systems10.5555/3288382.328840890:12(1729-1741)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.5555/3288382.3288408
Show More Cited By

Index Terms

Real-time detection of speed-limit traffic signs on the real road using Haar-like features and boosted cascade
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

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

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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Association for Computing Machinery

New York, NY, United States

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Published: 09 January 2014

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Ministry of Education, Science and Technology

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ICUIMC '14

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SIGAPP
SKKU

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

January 9 - 11, 2014

Siem Reap, Cambodia

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

Overall Acceptance Rate 251 of 941 submissions, 27%

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Triki NKarray MKsantini M(2023)A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart VehiclesApplied Sciences10.3390/app1308479313:8(4793)Online publication date: 11-Apr-2023
https://doi.org/10.3390/app13084793
Shi LZhou Y(2021)A CNN-based Traffic Sign Detection and Classification Method Using Priori Knowledge2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)10.1109/CONF-SPML54095.2021.00057(258-264)Online publication date: Nov-2021
https://doi.org/10.1109/CONF-SPML54095.2021.00057
Abdi LMeddeb A(2018)Spatially Enhanced Bags of Visual Words Representation to Improve Traffic Signs RecognitionJournal of Signal Processing Systems10.5555/3288382.328840890:12(1729-1741)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.5555/3288382.3288408
Abdi LMeddeb A(2018)In-vehicle augmented reality system to provide driving safety informationJournal of Visualization10.1007/s12650-017-0442-621:1(163-184)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s12650-017-0442-6
Abdi LMeddeb AShin SShin DLencastre M(2017)Deep learning traffic sign detection, recognition and augmentationProceedings of the Symposium on Applied Computing10.1145/3019612.3019643(131-136)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3019612.3019643

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A Real-Time Drivable Road Detection Algorithm in Urban Traffic Environment

Real-time traffic sign recognition in three stages

Real‐time dynamic vehicle detection on resource‐limited mobile platform

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