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JBE, vol. 23, no. 1, pp.93-103, January, 2018


Algorithm for Speed Sign Recognition Using Color Attributes and Selective Region of Interest

Ki Hun Park and Oh Seol Kwon

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This paper presents a method for speed limit sign recognition in images. Conventional sign recognition methods decreases recognition accuracy because they are very sensitive and include repeated features. The proposed method emphasizes color attributes based on the weighted YUV color space. Moreover, the recognition accuracy can be improved by extracting the local region of interest (ROI) in the candidates. The proposed method uses the Haar features and the Adaboost classifier for recognition. Experimental results confirm that the proposed algorithm is superior to conventional algorithms under various speed signs and conditions.

Keyword: Speed sign recognition, color attributes

[1] S. Maldonado-Bascon, S. Lafuente-Arroyo, P. Gil-Jimenez, H. Gomez-Moreno and F. Lopez -Ferreras, “Road-Sign Detection and Recognition Based on Support Vector Machines,” IEEE Tran. Intelligent Transportation Systems, Vol. 8, No. 2, pp. 264-278, 2007. [2] D. Ciresan, U. Meier, J. Masci and J. Schmidhuber, "A committee of neural networks for traffic sign classification," Proc. of the IEEE International Joint Conf. on Neural Networks, pp. 1918-1921, 2011. [3] FeiXiang Ren, Jinsheng Huang, Ruyi Jiang ancd Reinhard Klette, "General Traffic Sign Recognition by Feature Matching," 24th International Conference on Image and Vision Computing, pp. 409-414, 2009. [4] Fatin Zaklouta and Bogdan Stanciulescu, "Real-Time Traffic-Sign Recognition Using Tree Classifiers," IEEE Transactions on Intelligent Transportation systems, vol. 13, no. 4, pp 1507-1514, 2012. [5] G. JaWon, H. MinCheol, K. Byoung Chul and N. Jae-Yeal, “Real-time Speed-Limit Sign Detection and Recognition using Spatial Pyramid Feature and Boosted Random Forest,” 12th International Conference on Image Analysis and Recognition, pp. 437-445, July, 2015. [6] M. Mathias, R. Timofte, R. Benenson and L.V. Gool, “Traffic sign recognition—How far are we from the solution?,” IEEE Int. Con. Neural Networks, pp. 1-8, 2013. [7] N. Barnes, A Zelinsky and L.S. Fletcher, “Real-time speed sign detection using the radial symmetry detector,” IEEE Trans. Intelligent Transportation Systems, pp. 322-332, 2008. [8] B. Froba, and A. Ernst, "Face Detection with the Modified Census Transform," Proc. of the sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91-96. 2003. [9] Y. Aoyagi and T. Asakura. “Detection and recognition of traffic sign in scene image using genetic algorithms and neural networks.,” SICE-ANNUAL CONF. pp. 1343-1348, 1996. [10] D. Ciresan, U. Meier, J. Masci, and J. Schmidhuber, “A Committee of Neural Networks for Traffic Sign Classification,” International Joint Conference on Neural Networks, San Jose, USA, pp. 1918-1921, 2011. [11] K. Lim, Y. Hong, Y. Choi, and H. Byun, "Real-time traffic sign recognition based on a general purpose GPU and deep-learning," PLoS ONE, 12(3): e0173317, doi:10.1371/journal. pone.0173317, 2017 [12] S. Kang and D. Han. “Robust vehicle detection in rainy situation with Adaboost using CLAHE,” The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 12, pp. 1978-1984, 2016.


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