Multiple thresholding and subspace based approach for detection and recognition of traffic sign

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

Original languageEnglish
Pages (from-to)6973-6991
Number of pages19
JournalMultimedia Tools and Applications
Volume76
Issue number5
DOIs
Publication statusPublished - 01-03-2017

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Traffic signs
Advanced driver assistance systems
Support vector machines
Classifiers

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

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title = "Multiple thresholding and subspace based approach for detection and recognition of traffic sign",
abstract = "Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 {\%} and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.",
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Multiple thresholding and subspace based approach for detection and recognition of traffic sign. / Gudigar, Anjan; Chokkadi, Shreesha; Raghavendra, U.; Acharya, U. Rajendra.

In: Multimedia Tools and Applications, Vol. 76, No. 5, 01.03.2017, p. 6973-6991.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Multiple thresholding and subspace based approach for detection and recognition of traffic sign

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AU - Chokkadi, Shreesha

AU - Raghavendra, U.

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AB - Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

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