An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

Safat B. Wali, Hannan M A, Aini Hussain, Salina Abdul Samad

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

The main objective of this study is to develop an efficient TSDR system which contains an enriched dataset of Malaysian traffic signs. The developed technique is invariant in variable lighting, rotation, translation, and viewing angle and has a low computational time with low false positive rate. The development of the system has three working stages: image preprocessing, detection, and recognition. The system demonstration using a RGB colour segmentation and shape matching followed by support vector machine (SVM) classifier led to promising results with respect to the accuracy of 95.71%, false positive rate (0.9%), and processing time (0.43 s). The area under the receiver operating characteristic (ROC) curves was introduced to statistically evaluate the recognition performance. The accuracy of the developed system is relatively high and the computational time is relatively low which will be helpful for classifying traffic signs especially on high ways around Malaysia. The low false positive rate will increase the system stability and reliability on real-time application.

Original languageEnglish
Article number250461
JournalMathematical Problems in Engineering
Volume2015
DOIs
Publication statusPublished - 2015

Fingerprint

Color Segmentation
Traffic signs
Shape Matching
Support vector machines
Support Vector Machine
Traffic
Color
False Positive
System stability
Classifiers
Demonstrations
Lighting
Processing
Malaysia
Receiver Operating Characteristic Curve
Preprocessing
Classifier
Real-time
Angle
Invariant

ASJC Scopus subject areas

  • Mathematics(all)
  • Engineering(all)

Cite this

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