Abstract
Today’s modern human life-style is in a great uplift with respect to anything and everything. The requirement of road transportation and vehicles is increasing day-by-day which is proportional to the number of accidents happening eventually. At this juncture, building an intelligent vehicle system and other safety driven driver assistance systems have become the order-of-the-day. Despite the fact that considerable research has been carried out in detecting and tracking the traffic signs, still there is a huge lack in pertinent traffic sign recognition systems, especially in countries like India. This paper designates on the implementation of iReSign, next-generation two-tier identity classifier-based traffic sign recognition system architecture using Zernike and GFD algorithms for the future intelligent vehicle systems. Further, this paper details the results and its analysis, which is based on 840 images of 28 distinct traffic signs of India (collected manually). Using Zernike Moments and Generic Fourier Descriptors (GFD) region-based global shape recognition techniques, iReSign produced 78.33%, 90% cross validation accuracy and 77.85%, 91% testing recognition accuracy respectively. Using our new hybrid approach of combining Zernike Moments and GFD, iReSign produced 92% cross validation accuracy and 95% testing recognition accuracy.
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References
Official website of India - Delhi Traffic Police listing signs as per standards, http://www.delhitrafficpolice.nic.in/traffic-mandatory-signs.html/
Keerthi, B., Madhan, K.S., Sarukesi, K., Rodrigues, P.: Implementation of Next-generation Traffic Sign Recognition System with Two-tier Classifier Architecture. In: International Conference on Advances in Communications, Computing and Informatics, pp. 481–487. ACM, India (2012)
Yongping, W., Shimeiping, Tao, W.: A Method of Fast and Robust For Traffic Sign Recognition. In: 5th IEEE International Conference on Image and Graphics, pp. 891–895. IEEE Computer Society, China (2009)
Shi, M., Wu, H., Fleyeh, H.: A Robust Model for Traffic Signs recognition based on Support Vector Machines. In: International Congress on Image and Signal Processing, pp. 516–524. IEEE, China (2008)
Fleyeh, H., Dougherty, M., Aenugula, D., Baddam, S.: Invariant road sign recognition with Fuzzy ARTMAP and Zernike Moments. In: Intelligent Vehicles Symposium, pp. 31–35. IEEE Intelligent Transportation Systems Society, Istanbul (2007)
Hossain, M.S., Hasan, M.M., Ali, M.A., Kabir, M.H., Ali, A.B.M.S.: Automatic detection and recognition of Traffic signs. In: International Conference on Robotics, Automation and Mechatronics, pp. 286–291. IEEE, Singapore (2010)
Zhu, S., Liu, L., Lu, X.: Color Geometric Model for traffic sign recognition. In: IMACS Mulitconference on Computational Engineering in Systems Applications, pp. 2028–2032. IEEE, China (2006)
LIBSVM tool, http://www.csie.ntu.edu.tw/cjlin/libsvm/
Zhang, D., Lu, G.: Review of shape representation and description techniques. The Journal of the Pattern Recognition Society 37, 1–19 (2004)
Kan, C., Srinath, M.D.: Invariant Character recognition with Zernike and Orthogonal Fourier-Mellin moments. The Journal of the Pattern Recognition Society 35, 143–154 (2002)
Kotoulas, L., Dis, A.: Real-time Computation of Zernike Moments. IEEE Transactions on Circuits and System for Video Technology 15, 801–809 (2002)
Liu, L., Zhu, S.: Research of Intelligence Classifier for Traffic sign recognition. In: 6th International Conference on ITS Telecommunications, pp. 78–81. IEEE, China (2006)
Li, L., Li, J., Sun, J.: Traffic Sign classification based on Support vector machines and Tchebichef Moments. In: The International Conference on Computational Intelligence and Software Engineering, pp. 1–4. IEEE, China (2009)
Piccioli, G., De Micheli, E., Campani, M.: A Robust Method for Road Sign Detection and Recognition. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 800, pp. 493–500. Springer, Heidelberg (1994)
De la Escalera, A., Armingol, J.M., Pastor, J.M., Rodriguez, F.J.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 5, 57–68 (2004)
Gao, X.W., Podladchikova, L., Shaposhnikov, D., Hong, K., Shevtsova, N.: Recognition of traffic signs based on their colour and shape features extracted using human vision models. The Journal on Visual Communication and Image Representation 17, 675–685 (2006)
Ruta, A., Li, Y., Liu, X.: Real-time traffic sign recognition from video by class-specific discriminative features. The Journal of the Pattern Recognition Society 43, 416–430 (2010)
Zhang, D., Lu, G.: Generic Fourier descriptor for shape-based image retrieval. In: IEEE International Conference on Multimedia and Expo., pp. 425–428. IEEE, Switzerland (2002)
GNU plot, http://www.gnuplot.info/
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Balasundaram, K., Srinivasan, M.K., Sarukesi, K. (2012). iReSign – Implementation of Next-Generation Two-Tier Identity Classifier-Based Traffic Sign Recognition System Architecture Using Hybrid Region-Based Shape Representation Techniques. In: Thampi, S.M., Zomaya, A.Y., Strufe, T., Alcaraz Calero, J.M., Thomas, T. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2012. Communications in Computer and Information Science, vol 335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34135-9_40
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DOI: https://doi.org/10.1007/978-3-642-34135-9_40
Publisher Name: Springer, Berlin, Heidelberg
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