Road Signs Detection and Reconstruction using Gielis Curves

Valentine Vega (1) , Désiré Sidibé (1) , Yohan Fougerolle (1)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]

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.

Mots clés: Gielis curves; Road sign detection; Color segmentation; Contour fitting; Gielis curves.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Valentine Vega, Désiré Sidibé, Yohan Fougerolle. Road Signs Detection and Reconstruction using Gielis Curves. International Conference on Computer Vision Theory and Applications, Feb 2012, Rome, Italy. pp.393-396. ⟨hal-00658085⟩