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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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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