Abstract
Road signs recognition systems are developed to assist drivers and to help increase traffic safety. Shape detectors constitute a front-end in majority of such systems. In this paper we propose a method for robust detection of triangular, rectangular and rhombus shaped road signs in real traffic scenes. It starts with segmentation of colour images. For this purpose the histograms were created from hundreds of real warning and information signs. Then the characteristic points are detected by means of the developed symmetrical detector of local binary features. The points are further clusterized and used to select shapes from the input images. Finally, the shapes are verified to fulfil geometrical properties defined for the road signs. The proposed detector shows high accuracy and very fast operation time what was verified experimentally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cyganek, B.: Rotation Invariant Recognition of Road Signs with Ensemble of 1-NN Neural Classifiers. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 558–567. Springer, Heidelberg (2006)
Cyganek, B.: Recognition of Road Signs with Mixture of Neural Networks and Arbitration Modules. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 52–57. Springer, Heidelberg (2006)
Cyganek, B.: Circular Road Signs Recognition with Soft Classifiers. Accepted to the Integrated Computer-Aided Engineering. IOS Press, Amsterdam (2007)
Chrysler, D.: The Thinking Vehicle (2002), http://www.daimlerchrysler.com
Escalera, A., Armingol, J.A.: Visual Sign Information Extraction and Identification by Deformable Models. IEEE Tr. On Int. Transportation Systems 5(2), 57–68 (2004)
Fleyeh, H., Gilani, S.O., Dougherty, C.: Road Sign Detection And Recognition Using Fuzzy Artmap. In: IASTED Int. Conf. on Art. Intell. and Soft Computing, pp. 242–249 (2006)
Gao, X.W., Podladchikova, L., Shaposhnikov, D., Hong, K., Shevtsova, N.: Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication & Image Representation, 675–685 (2005)
Gavrila, D.M.: Multi-feature Hierarchical Template Matching Using Distance Transforms. In: Proc. of the Int. Conf. on Pattern Recognition, Brisbane, pp. 439–444 (1998)
Paclik, P., Novovicova, J., Pudil, P., Somol, P.: Road sign classification using Laplace kernel classifier. Pattern Recognition Letters 21, 1165–1173 (2000)
Piccioli, G., Micheli, E.D., Parodi, P., Campani, M.: Robust method for road sign detection and recognition. Image and Vision Computing 14, 209–223 (1996)
Road Signs and Signalization. Directive of the Polish Ministry of Infrastructure, Internal Affairs and Administration (Dz. U. Nr 170, poz. 1393) (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cyganek, B. (2007). Real-Time Detection of the Triangular and Rectangular Shape Road Signs. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_67
Download citation
DOI: https://doi.org/10.1007/978-3-540-74607-2_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
eBook Packages: Computer ScienceComputer Science (R0)