Abstract
This paper presents an automatic road sign recognition system. The system bases on twodimensional hidden Markov models. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the created road sign recognition system is described. The system is able to recognize the road signs, which was detected earlier. The system makes use of two dimensional discrete wavelet transform for features extraction of road signs. In recognition process system bases on two dimensional hidden Markov models. The experimental results demonstrate that the system is able to achieving an average recognition rate of 83% using the two-dimensional hidden Markov models and the wavelet transform.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Database German Traffic Sign Benchmark, http://benchmark.ini.rub.de/Dataset/GTSRB-Final-TrainingImages.zip (April 20, 2014)
Eickeler, S., Mller, S., Rigoll, G.: High Performance Face Recognition Using Pseudo 2-D Hidden Markov Models. In: European Control Conference (1999), http://citeseer.ist.psu.edu
de la Escalera, A., Moreno, L., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE Transaction Industrial Electronics 44(6), 848–859 (1997)
Forney, G.D.: The Viterbi Algorithm. Proc. IEEE 61(3), 268–278 (1973)
Garcia-Garrido, M., Sotelo, M., MartinGorostiza, E.: Fast traffic sign detection and recognition under changing lighting conditions. In: Sotelo, M. (ed.) Proceedings of the IEEE ITSC, pp. 811–816 (2006)
Gomez-Moreno, H., Lopez-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Transaction on Intelligent Transportation Systems 8(2), 264–278 (2007)
Hsien, J.C., Liou, Y.S., Chen, S.Y.: Road Sign Detection and Recognition Using Hidden Markov Model. Asian Journal of Health and Information Sciences 1(1), 85–100 (2006)
Hsu, S.H., Huang, C.L.: Road sign detection and recognition using matching pursuit method. Image and Vision Computing 19, 119–129 (2001)
Joshi, D., Li, J., Wang, J.Z.: A computationally Efficient Approach to the estimation of two- and three-dimensional hidden Markov models. IEEE Transactions on Image Processing 15(7), 1871–1886 (2006)
Kanungo, T.: Hidden Markov Model Tutorial (1999), http://www.kanungo.com/software/hmmtut.pdf
Kubanek, M.: Automatic Methods for Determining the Characteristic Points in Face Image. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS (LNAI), vol. 6113, pp. 523–530. Springer, Heidelberg (2010)
Li, J., Najmi, A., Gray, R.M.: Image classification by a two dimensional Hidden Markov model. IEEE Transactions on Signal Processing 48, 517–533 (2000)
Maldonado-Bascn, S., Acevedo-Rodrguez, J., Lafuente-Arroyo, S., FernndezCaballero, A., Lpez-Ferreras, F.: An optimization on pictogram identification for the road-sign recognition task using SVMs. Computer Vision and Image Understanding 114(3), 373–383 (2010)
Nguwi, Y.Y., Cho, S.Y.: Emergent selforganizing feature map for recognizing road sign images. Neural Computing and Application 19, 601–615 (2010)
Pazhoumand-dar, H., Yaghoobi, M.: A new approach in road sign recognition based on fast fractal coding. Neural Computing & Application 22, 615–625 (2013)
Piccioli, G., Micheli, E.D., Parodi, P., Campani, M.: Robust method for road sign detection and recognition. Image Vision and Computing 14, 209–223 (1996)
Prietoa, M.S., Allen, A.R.: Using selforganising maps in the detection and recognition of road signs. Image and Vision Computing 27(6), 673–683 (2009)
Rabiner, L.R.: A tutorial on hidden Markov models and selected application in speech recognition. Proc. IEEE 77, 257–285 (1989)
Samaria, F., Young, S.: HMM-based Architecture for Face Identification. Image and Vision Computing 12(8), 537–583 (1994)
Smorawa, D., Kubanek, M.: Analysis of advanced techniques of image processing based on automatic detection system and road signs recognition. Journal of Applied Mathematics and Computational Mechanics 13(1) (2014)
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1453–1460 (2011)
Vicen-Bueno, R., Gil-Pita, R., Rosa-Zurera, M., Utrilla-Manso, M., López-Ferreras, F.: Multilayer perceptrons applied to traffic sign recognition tasks. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 865–872. Springer, Heidelberg (2005)
Vitoantonio Bevilacqua, V., Cariello, L., Carro, G., Daleno, D., Mastronardi, G.: A face recognition system based on Pseudo 2D HMM applied to neural network coefficients. Soft Computing 12(7), 615–621 (2008)
Yujian, L.: An analytic solution for estimating two-dimensional hidden Markov models. Applied Mathematics and Computation 185, 810–822 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bobulski, J. (2015). Two-Dimensional Hidden Markov Models in Road Signs Recognition. In: Choraś, R. (eds) Image Processing & Communications Challenges 6. Advances in Intelligent Systems and Computing, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-10662-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-10662-5_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10661-8
Online ISBN: 978-3-319-10662-5
eBook Packages: EngineeringEngineering (R0)