Authors:
Valentine Véga
1
;
Désiré Sidibé
2
and
Yohan Fougerolle
2
Affiliations:
1
Universitas Gunadarma and Université de Bourgogne, Indonesia
;
2
Université de Bourgogne, France
Keyword(s):
Road Sign Detection, Color Segmentation, Contour Fitting, Gielis Curves.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
Road signs are among the most important navigation tools in transportation systems. The identification of road signs in images is usually based on first detecting road signs location using color and shape information. In this paper, we introduce such a two-stage detection method. Road signs are located in images based on color segmentation, and their corresponding shape is retrieved using a unified shape representation based on Gielis curves. The contribution of our approach is the shape reconstruction method which permits to detect any common road sign shape, i.e. circle, triangle, rectangle and octagon, by a single algorithm without any training phase. Experimental results with a dataset of 130 images containing 174 road signs of various shapes, show an accurate detection and a correct shape retrieval rate of 81.01% and 80.85% respectively.