Abstract
This paper presents the method to classify the speed limit traffic sign using multiple features, namely histogram of oriented gradient (HOG) and maximally stable extremal regions (MSER) features. The classification process is divided into the outer circular ring matching and the inner part matching. The HOG feature is employed to match the outer circular ring of the sign, while MSER feature is employed to extract the digit number in the inner part of the sign. Both features are extracted from the grayscale image. The algorithm detects the rotation angle of the sign by analyzing the blobs which is extracted using MSER. In the matching process, tested images are matched with the standard reference images by calculating the Euclidean distance. The experimental results show that the proposed method for matching the outer circular ring works properly to recognize the circular sign. Further, the digit number matching achieves the high classification rate of 93.67% for classifying the normal and rotated speed limit signs. The total execution time for classifying six types of speed limit sign is 10.75 ms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bilgin, E., Robila, S.: Road sign recognition system on Raspberry Pi. In: 2016 IEEE Long Island Systems, Applications and Technology Conference, New York, USA, pp. 1–5 (2016)
Hoang, A.T., Koide, T., Yamamoto, M.: Real-time speed limit traffic sign detection system for robust automotive environments. IEIE Trans. Smart Process. Comput. 4(4), 237–250 (2015)
Biswas, R., Fleyeh, H., Mostakim, M.: Detection and classification of speed limit traffic signs. In: 2014 World Congress on Computer Applications and Information Systems (WCCAIS), Hammamet, Tunisia, pp. 1–6 (2014)
Peemen, M., Mesman, B., Corporaal, H.: Speed sign detection and recognition by convolutional neural networks. In: 8th International Automotive Congress, Eindhoven, Netherland, pp. 162–170 (2011)
Ali, F.H., Ismail, M.H.: Speed limit road sign detection and recognition system. Int. J. Comput. Appl. 131(2), 43–50 (2015)
Kundu, S.K., Mackens, P.: Speed limit sign recognition using MSER and artificial neural networks. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Spain, pp. 1849–1854 (2015)
Malik, R., Khurshid, J., Ahmad, S.N.: Road sign detection and recognition using colour segmentation, shape analysis and template matching. In: 2007 International Conference on Machine Learning and Cybernetics, Hong Kong, pp. 3556–3560 (2007)
Laguna, R., Barrientos, R., Blazquez, L.F., Miguel, L.J.: Traffic sign recognition application based on image processing techniques. In: The 19th World Congress. The International Federation of Automatic Control, Cape Town, South Africa, pp. 104–109 (2014)
Soetedjo, A., Yamada, K.: Traffic sign classification using ring-partitioned matching. IEICE Trans. Fundam. E88(A9), 2419–2426 (2005)
Adam, A., Ioannidis, C.: Automatic road-sign detection and classification based on support vector machines and HOG descriptors. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. II 2(5), 1–7 (2014)
Greenhalgh, J., Mirmehdi, M.: Real-time detection and recognition of road traffic signs. IEEE Trans. Intell. Transp. Syst. 13(4), 1498–1506 (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA, vol. 1, pp. 886–893 (2005)
Matas, J.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The IEEE International Joint Conference on Neural Networks, San Jose, CA, USA, pp. 1453–1460 (2011)
Acknowledgements
This work is supported by the Research Grant 2017, Competence-based research scheme from Directorate General of Higher Education, Ministry of Research and Technology and Higher Education, Republic of Indonesia, No.: SP DIPA-042.06.1.401516/2017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Soetedjo, A., Somawirata, I.K. (2018). Speed Limit Traffic Sign Classification Using Multiple Features Matching. In: Kim, K., Kim, H., Baek, N. (eds) IT Convergence and Security 2017. Lecture Notes in Electrical Engineering, vol 449. Springer, Singapore. https://doi.org/10.1007/978-981-10-6451-7_26
Download citation
DOI: https://doi.org/10.1007/978-981-10-6451-7_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6450-0
Online ISBN: 978-981-10-6451-7
eBook Packages: EngineeringEngineering (R0)