Road sign detection and recognition from video stream using HSV, contourlet transform and local energy based shape histogram

Zakir, U.; Edirishinghe, E.A.; Hussain, A.

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.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 5th International Conference, BICS 2012
• Start Date: Jul 11, 2012
• End Date: Jul 14, 2012
• Publication Date: 2012
• Deposit Date: Oct 15, 2019
• Publisher: Springer
• Pages: 411-419
• Series Title: Lecture Notes in Computer Science
• Series Number: 7366
• Series ISSN: 0302-9743
• Book Title: Advances in Brain Inspired Cognitive Systems
• ISBN: 978-3-642-31560-2
• DOI: https://doi.org/10.1007/978-3-642-31561-9_46
• Keywords: Road Signs, HSV, Contourlet Transform, LESH, Autonomous Vehicles
• Public URL: http://researchrepository.napier.ac.uk/Output/1793240