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Real-time Traffic Sign Recognition using Rotation-invariant Fast Binary Patterns
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  • Journal title : Journal of Broadcast Engineering
  • Volume 21, Issue 4,  2016, pp.562-568
  • Publisher : The Korean Institute of Broadcast and Media Engineers
  • DOI : 10.5909/JBE.2016.21.4.562
 Title & Authors
Real-time Traffic Sign Recognition using Rotation-invariant Fast Binary Patterns
Hwang, Min-Chul; Ko, Byoung Chul; Nam, Jae-Yeal;
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In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.
Traffic sign recognition;rotation-invariant binary patterns;random forest;convolutional neural networks;
 Cited by
J. Stallkamp, M. Schlipsing, J. Salmen and C. Igel, “The German traffic sign recognition benchmark: a multi-class classification competition,” IEEE Int. Conf. Neural Networks, pp. 1453-1460, 2011.

D. Cireşan, U. Meier, J. Masci and J. Schmidhuber, “A committee of neural networks for traffic sign classification,” IEEE Int. Conf. Neural Networks, pp. 1918-1921, Aug. 2011.

Y.Wu, Y. Liu,J.n Li, H. Liu,and X. Hu, "Traffic sign detection based on convolutional neural networks," IEEE Int. Conf. Neural Networks, pp.1-7, Aug. 2013.

Y. Zeng, X. Xu, Y. Fang, K. Zhao, "Traffic Sign Recognition Using Deep Convolutional Networks and Extreme Learning Machine," Lecture Notes in Computer Science, vol. 9242, pp. 272-280, 2015.

Y. Zeng, X. Xu, Y. Fang, and K. Zhao, “Traffic Sign Recognition Using Extreme Learning Classifier with Deep Convolutional Features,” The 2015 Int. Conf. Intelligence Science and Big Data Engineering, pp. 1-10, June, 2015.

J. W. Gim, M. C. Hwang, B.C. Ko andJ. Y. Nam, “Real-time Speed-Limit Sign Detection and Recognition using Spatial Pyramid Feature and Boosted Random Forest,” 12th Int. Conf. Image Analysis and Recognition,pp.437-445, July, 2015.

S. Liao, X. Zhu, Z. Lei, L. Zhang and S.Z. Li,“Learning multi-scale block local binary patterns for face recognition,” Journal of Biometrics, pp. 828-837, 2007.

S. Yin, P. Ouyang, L. Liu, Y. Guo and S. Wei,“Fast traffic sign recognition with a rotation invariant binary pattern based feature,” Journal of Sensors, vol. 15, pp. 2161-2180, 2015. crossref(new window)

L. Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001. crossref(new window)

S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing and C. Igel, “Detection of traffic signs in real-world images: The German traffic sign detection benchmark,” IEEE Int. Conf. Neural Networks, pp. 1-8, Aug. 2013.

P.Sermanet,Y. LeCun, “Traffic sign recognition with multi-scale convolutional networks,” IEEE Int. Conf. Neural Networks , pp. 2809-2813, Aug. 2011.

F. Zaklouta, B. Stanciulescu, and O. Hamdoun, “Traffic sign classification using k-d trees and random forests,” IEEE Int. Conf. Neural Networks, pp. 2151-2155, Aug. 2011.