Distortion invariant road sign detection

Authors:
Jesmin F. Khan;Sharif M. A.Bhuiyan;Reza R. Adhami
Affiliations:
University of Alabama in Huntsville, Department of Electrical and Computer Engineering, Huntsville, AL;University of Alabama in Huntsville, Department of Electrical and Computer Engineering, Huntsville, AL;University of Alabama in Huntsville, Department of Electrical and Computer Engineering, Huntsville, AL
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
Cited 0

Visual sign information extraction and identification by deformable models for intelligent vehicles

IEEE Transactions on Intelligent Transportation Systems
Detection of text on road signs from video

IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines

IEEE Transactions on Intelligent Transportation Systems
A hierarchical approach to color image segmentation using homogeneity

IEEE Transactions on Image Processing
Automatic detection and recognition of signs from natural scenes

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.03

Visualization

Abstract

This paper proposes an universal method to detect and recognize road signs of any countries with any color or any of the existing shapes (e.g. circular, rectangular, triangular, pentagonal and octagonal). The presented system is invariant to transformation (e.g. translation, rotation, scale and occlusion). There are three main stages in the proposed algorithm: 1) segmentation based on the color features to find the the region of interests (ROIs), 2) traffic sign detection by using two novel shape classification criteria, and 3) recognition of the road sign using distortion invariant fringe-adjusted joint transform correlation (FJTC) for matching the unknown signs with the known reference road signs stored in the database. Experimental results on real life images demonstrate that the proposed framework is invariant to translation, rotation, scale and partial occlusions.