Abstract
This paper describes an efficient approach towards road sign detection and recognition. The proposed system is divided into three sections namely; Colour Segmentation of the road traffic signs using the HSV colour space considering varying lighting conditions, Shape Classification using the Contourlet Transform considering occlusion and rotation of the candidate signs and the Recognition of the road traffic signs using features of a Local Energy based Shape Histogram (LESH). We have provided three experimental results and a detailed analysis to justify that the algorithm described in this paper is robust enough to detect and recognize road signs under varying weather, occlusion, rotation and scaling conditions using video stream.
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Zakir, U., Zafar, I., Edirisinghe, A.E.: Road Sign Detection and Recognition by using Local Energy Based Shape Histogram (LESH). International Journal of Image Processing 4(6), 566–582 (2011) ISSN: 1985-2304
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© 2012 Springer-Verlag Berlin Heidelberg
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Zakir, U., Edirishinghe, E.A., Hussain, A. (2012). Road Sign Detection and Recognition from Video Stream Using HSV, Contourlet Transform and Local Energy Based Shape Histogram. In: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_46
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DOI: https://doi.org/10.1007/978-3-642-31561-9_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31560-2
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