ROAD SIGN DETECTION AND SHAPE RECONSTRUCTION

USING GIELIS CURVES

Valentine Vega

1,2

, D

esir

e Sidib

and Yohan Fougerolle

Universitas Gunadarma, Jalan Margonda Raya 100, Depok, 16424 West Java, Indonesia

Universit

e de Bourgogne, Laboratoire Le2i, UMR CNRS 5158, 12 Rue de la Fonderie, 71200 Le Creusot, France

Keywords:

Road Sign Detection, Color Segmentation, Contour Fitting, Gielis Curves.

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.

1 INTRODUCTION

Intelligent Transportation System (ITS) has been a

rapidly growing topic of research over the last years.

One important component of any ITS is the capability

of automatic road sign recognition. The detection and

identiﬁcation of road sign is useful in highway main-

tenance, sign inventory or driver support systems.

A road sign recognition system is composed of

two main stages: the detection and the recogni-

tion (Bascon et al., 2007). Detection is performed

to obtain an initial candidate of road sign, i.e. the

possible region that represents road sign characteris-

tics, using either color or shape information. Conse-

quently, it requires a recognizer equipped with a set

of features or patterns (Bascon et al., 2007; Escalera

et al., 2003). Commonly used classiﬁers include neu-

ral network (Bascon et al., 2007), clustering (Escalera

et al., 2003), nearest neighbor (Escalera et al., 2004),

Laplace kernel (Fang et al., 2003), and support vector

machines (Nguwi and Kouzani, 2008).

In this paper we focus on the detection stage of

road signs in still images or videos. Several prob-

lems on detecting road signs in natural scene images

include misdetection, occlusions, motions blur issues

and scale variations.

The two main features used to detect and recog-

nize road signs are the color and the shape. Color is a

powerful cue for object detection, but is sensitive to

image acquisition conditions. On the other hand, the

shape is invariant to illumination, but sensitive to oc-

clusions and perspective distortions. Color based de-

tection methods are based on color segmentation. For

example, (Varun et al., 2007) use a simple threshold

formula applied to the red color channel to detect red

road signs. Several color spaces can be used, includ-

ing HSV (Paclik et al., 2000), HSI (Escalera et al.,

2003; Fang et al., 2003), CIECAM97 (Gao et al.,

2006) and IHSI (Fleyeh, 2004).

Shape based methods have strong robustness to

changing illumination as they detect shapes based on

edges. The most recurrent method is certainly the

generalized Hough transform (Ballard, 1981). Re-

cently, (Loy and Zelinsky, 2002) propose the fast ra-

dial symmetry transform which is a technique similar

to Hough transform. The method was extended and

successfully used to detect regular polygons by (Loy

and Barnes, 2004). (Gavrila, 1999) uses a template

matching approach for shape detection. First, edges in

the original image are found. Then, a distance trans-

form image is created and match against a reference

template (for instance, a triangle).

In this paper, we propose a robust road sign detec-

tion method which uses the color information to local-

ize potential road signs and then, based on shape rep-

resentation using Gielis curves (Gielis, 2003), iden-

tiﬁes the shape of the detected signs. The major

advantage of our approach is that it does not need

393

Véga V., Sidibé D. and Fougerolle Y..

ROAD SIGN DETECTION AND SHAPE RECONSTRUCTION USING GIELIS CURVES.

DOI: 10.5220/0003802003930396

In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP-2012), pages 393-396

ISBN: 978-989-8565-03-7

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

a multi-layer architecture as used in machine learn-

ing approaches (neural network or support vector ma-

chines). Furthermore, no initial training is needed.

Hence, the method is simple, fast, and is able to iden-

tify any common road sign shape (triangle, rectan-

gle, octagon and circle). Additionally, the proposed

method is scale invariant and accurately detects road

signs of different sizes.

The paper is organized as follows: The color seg-

mentation method adopted in our work is then de-

scribed in Section 2. Section 2 introduces Gielis

curves and the road sign shape identiﬁcation algo-

rithm. Experimental results are shown in Section 3

before our conclusions in the last section.

2 ROAD SIGN DETECTION AND

SHAPE RECONSTRUCTION

The ﬁrst step of the proposed method is the local-

ization of potential road signs in the image through

color segmentation. For robustness against lighting

variations, the Improved Hue, Luminance and Satu-

ration (IHLS) color space is selected. Once an image

is converted to IHLS color space, potential road signs

are detected using the segmentation method ﬁrst in-

troduced by (Escalera et al., 2003) and also used by

(Fleyeh, 2004).

Figure 1 shows some segmentation results. As

can be seen, road signs are correctly localized with

the segmentation method in most cases. However, in

some situations, because of lighting changes or occlu-

sion, road signs are not entirely detected. Thus, some

post-processing steps are necessary to help the shape

reconstruction algorithm.

Introduced by (Gielis, 2003), the superformula ex-

tends the superellipses by introducing variable rota-

tional symmetry and asymmetric shape coefﬁcients.

The angle φ is replaced by

mφ

to obtain m rotational

symmetries, and the unique shape coefﬁcient n for su-

perellipses is replaced by a triplet (n

, n

), leading

to the following radial polar parameterization:

r(φ) =



cos



mφ





sin



mφ





. (1)

a, b, and n

are positive real numbers (a, b, n

∈ R

a and b control the scale while n

, n

, and n

con-

trol the shape. m ∈ R

∗

represents the number of ro-

tational symmetries. By modifying the shape param-

eters, the set of the regular polygons can be gener-

ated. Three additional coefﬁcients T

, T

and φ

, cor-

responding to the position of the shape in the image

Figure 1: Example of segmentation results.

and its angular offset are added to represent Gielis

curves in the general case. Consequently, the pa-

rameters set Λ of a Gielis curve is deﬁned as Λ =

{a, b, n

, n

, m, T

, T

, φ

The process of 2D data representation by

Gielis curves starts by building a signed potential

ﬁeld (Fougerolle et al., 2005). A potential ﬁeld is a

signed function F which characterizes the inside and

outside of a closed object, such that F(x) is positive

if a point x lies within the object, F(x) is negative if x

lies on the outside, and F(x) = 0 if x lies on the curve.

The potential ﬁeld is used to build a cost func-

tion to be minimized in the least-squared sense using

Levenberg-Marquardt (LM) algorithm as illustrated

in Figure 2.

Figure 2: Levenberg-Marquardt: evolution of the curve.

3 EXPERIMENTS

We evaluate the performance of the proposed ap-

proach on a dataset of 130 images containing 174

signs of various shapes and types. The dataset is a

subset of the one used by (Bascon et al., 2007).

Figures 3, 4, 5, and 6 show some detection re-

sults for circular, octogonal, rectangular and triangu-

VISAPP 2012 - International Conference on Computer Vision Theory and Applications

394

lar signs, respectively. As can be seen in these im-

ages, the shape reconstruction method can success-

fully identify different road sign types. It is important

to note that the algorithm accurately detects road sign

of different sizes in the image. This is a clear advan-

tage over the method developped by (Loy and Barnes,

2004) where different radii are tried in order to ﬁnd

the correct size of road signs.

Figure 3: Detection for circular road signs.

Figure 4: Detection for octogonal road signs.

Figure 5: Detection for rectangular road signs.

Detection results using the entire dataset are sum-

marized in Table 1. From the 174 road signs, 141 are

correctly detected and 114 road sign shapes are suc-

cessfully reconstructed. Undesired detection occurs

for 23 objects having similar color with road signs,

while 27 road signs are incorrectly reconstructed. The

percentage of road sign detection and shape recon-

struction results are listed in Table 1. For each refer-

ence color, we calculate the percentage from the num-

ber of detected (reconstructed) road signs divided by

the total number of road signs of that particular color.

The total percentage is calculated from the number of

detected (reconstructed) road signs in both colors di-

vided by the total number of road signs in the images.

Note that the performance of the shape reconstruc-

tion method (identiﬁcation of road sign shape) is di-

rectly related to the performance of the detection step

(color segmentation). Indeed, the shape of a road sign

which is not detected in the segmentation step will ob-

viously not be reconstructed. Several road signs in the

dataset appear at a far distance from the camera, and

can be eliminated in the post-processing stage, lower-

ing detection rate.

Incorrect reconstructions are due to different

causes including incorrect segmentation, wrong ini-

tialization of the ﬁtting algorithm and perspective dis-

tortion as illustrated in Figure 7.

Figure 6: Detection for triangular road signs.

(a) Multiple parts extraction.

(b) High perspective distorsions.

(d) Occlusions.

Figure 7: Examples of wrong reconstruction.

ROAD SIGN DETECTION AND SHAPE RECONSTRUCTION USING GIELIS CURVES

395

Table 1: Summary of detection and reconstruction results.

Detection (%) Reconstruction (%)

detected not detected correct incorrect

Red Signs 81.43 18.57 81.58 18.42

Blue Signs 79.41 20.59 77.78 22.22

TOTAL 81.03 18.97 80.85 19.15

4 CONCLUSIONS

In this paper, a novel road sign detection method is

proposed. The method ﬁrst uses color information to

localize potential road signs and then, identiﬁes the

correct shape of the detected signs. Shape detection

is based on shape representation using Gielis curves

which provides an elegant way to handle all common

road sign types, i.e. triangles, rectangles, octagons,

and ellipses. Experimental results show the robust-

ness of the approach in detecting trafﬁc signs of var-

ious shapes. The method is invariant to in-plane ro-

tation and to small perspective distortion due to the

introduction of a rotational offset as a parameter in

the ﬁtting algorithm. It is also able to detect signs

of different sizes in the image. The different causes

of failure can be considered by improving the color

segmentation method robustness to illumination ef-

fect. We could for example apply a color constancy

algorithm prior to segmentation. Another improve-

ment would be the introduction of a parameter in the

ﬁtting algorithm to account for strong perspective dis-

tortions. Eventually, the general least square formu-

lation of the problem could be replaced by a more ro-

bust version, using M-estimators for instance, in or-

der to better handle outliers to improve the results in

presence of degenerate contours due to occlusions or

strong light variations.

REFERENCES

Ballard, D. H. (1981). Generalizing the hough trans-

form to detect arbitrary shapes. Pattern Recognition,

13(2):111–122.

Bascon, S. M., Arroyo, S. L., Jimenez, P. G., Moreno, H. G.,

and Ferreras, F. L. (2007). Road Sign Detection and

Recognition Based on Support Vector Machines. In

IEEE Transactions on Intelligent Transportation Sys-

tem, volume 8, pages 264–278.

Escalera, A., Armingol, J. M., and Mata, M. (2003). Trafﬁc

Sign Recognition and Analysis for Intelligent Vehi-

cles. Image and Vision Computing, 21:247–258.

Escalera, A., Armingol, J. M., Pastor, J. M., and Rodriguez,

F. J. (2004). Visual Sign Information Extraction and

Identiﬁcation by Deformable Models for Intelligent

Vehicles. In IEEE Transactions on Intelligent Trans-

portation Systems, volume 5, pages 57–68.

Fang, C. Y., Chen, S. W., and Fuh, C. S. (2003). Road Sign

Detection and Tracking. In IEEE Transactions on Ve-

hicular Technology, volume 52, pages 1329–1341.

Fleyeh, H. (2004). Color Detection and Segmentation for

Road and Trafﬁc Signs. In Proceedings of the 2004

IEEE Conference on Cybernetics and Intelligent Sys-

tems, pages 808–813, Singapore.

Fougerolle, Y. D., Gribok, A., Foufou, S., Truchetet, F.,

and Abidi, M. A. (2005). Boolean Operations with

Implicit and Parametric Representations of Primitives

using R-functions. In IEEE Transactions on Visualisa-

tion and Computer Graphics, volume 11, pages 529–

539.

Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K.,

and Shevtsova, N. (2006). Recognition of trafﬁc signs

based on their colour and shape features extracted us-

ing human vision models. Journal of Visual Commu-

nication and Image Representation, 17(4):675–685.

Gavrila, D. (1999). Trafﬁc sign recognition revisited. In

GAGM-Symposium, pages 86–93.

Gielis, J. (2003). A Generic Geometric Transformation that

Uniﬁes a Wide Range of Natural and Abstract Shapes.

American Journal Botany, 90:333–338.

Loy, G. and Barnes, N. (2004). Fast shape-based road sign

detection for driver assistance system. In Proceedings

of International Conference on Intelligent Robots and

Systems (IROS 04), pages 70–75.

Loy, G. and Zelinsky, A. (2002). A fast radial symmetry

transform for detecting points of interest. In Proceed-

ings of 7th European Conference on Computer Vision,

pages 358–368.

Nguwi, Y. Y. and Kouzani, A. Z. (2008). Detection and

Classiﬁcation of Road Signs in Natural Environment.

In Neural Compututing and Application, pages 265–

289. Springer-Verlag London Limited 2007.

Paclik, P., Novovicova, J., Pudil, P., and Somol, P. (2000).

Road sign classifﬁcation using laplace kernel classi-

ﬁer. Pattern Recognition Letters, 21:1165–1173.

Varun, S., Singh, S., Kunte, R. S., Samuel, R. D. S., and

Philip, B. (2007). A road trafﬁc signal recognition sys-

tem based on template matching employing tree clas-

siﬁer. In Proceedings of International Conference on

Computational Intelligence and Multimedia Applica-

tions (ICCIMA 07), pages 360–365, Washington, DC,

USA.

VISAPP 2012 - International Conference on Computer Vision Theory and Applications

396