Abstract
The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.
A. Youssef and D. Albani—These two authors contributed equally to the work.
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References
Barnes, N., Zelinsky, A.: Real-time radial symmetry for speed sign detection. In: Intelligent Vehicles Symposium, pp. 566–571 (2004)
Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: CVPR, pp. 2903–2910 (2012)
Ciresan, D.C., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC (2009)
Dollr, P., Appel, R., Kienzle, W.: Crosstalk cascades for frame-rate pedestrian detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. Lecture Notes in Computer Science, vol. 7573, pp. 645–659. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33709-3_46
Doman, K., Deguchi, D., Takahashi, T., Mekada, Y., Ide, I., Murase, H., Sakai, U.: Estimation of traffic sign visibility considering local and global features in a driving environment. In: Intelligent Vehicles Symposium, pp. 202–207 (2014)
Fleyeh, H.: Color detection and segmentation for road and traffic signs. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems, pp. 809–814 (2004)
Hanbury, A.: A 3D-polar coordinate colour representation well adapted to image analysis. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 804–811. Springer, Heidelberg (2003). doi:10.1007/3-540-45103-X_107
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in neural information processing systems, pp. 1097–1105 (2012)
Mathias, M., Timofte, R., Benenson, R., Van-Gool, L.: Traffic sign recognition how far are we from the solution? In: IJCNN, pp. 1–8 (2013)
Overett, G., Tychsen-Smith, L., Petersson, L., Pettersson, N., Andersson, L.: Creating robust high-throughput traffic sign detectors using centre-surround hog statistics. Mach. Vis. Appl. 25, 713–726 (2014)
Perona, P.: Visual recognition, Circa 2007. In: Object Categorization Computer and Human Vision Perspectives, pp. 55–68. Cambridge University Press (2009)
Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: IJCNN, pp. 2809–2813 (2011)
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323–332 (2012)
Tan, M., Wang, B., Wu, Z., Wang, J., Pan, G.: Weakly supervised metric learning for traffic sign recognition in a LIDAR-equipped vehicle. IEEE Trans. Intell. Transp. Syst. 17(5), 1415–1427 (2016)
Timofte, R., van Gool, L.: Sparse representation based projections. In: BMVC, pp. 61.1–61.12 (2011)
Timofte, R., van Gool, L.: Iterative nearest neighbors for classification and dimensionality reduction. In: CVPR, pp. 2456–2463 (2012)
Vega, V., Sidibé, D., Fougerolle, Y.: Road signs detection and reconstruction using gielis curves. In: International Conference on Computer Vision Theory and Applications, pp. 393–396 (2012)
Wang, D., Yue, S., Xu, J., Hou, X., Liu, C.-L.: A saliency-based cascade method for fast traffic sign detection. In: Intelligent Vehicles Symposium, pp. 180–185 (2015)
Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using deep convolutional networks and extreme learning machine. In: IScIDE, pp. 272–280 (2015)
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Youssef, A., Albani, D., Nardi, D., Bloisi, D.D. (2016). Fast Traffic Sign Recognition Using Color Segmentation and Deep Convolutional Networks. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_19
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DOI: https://doi.org/10.1007/978-3-319-48680-2_19
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