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Abstract

Traffic sign recognition is important for the development of driver assistance systems and fully autonomous vehicles. Even though GPS navigator systems works well for most of the time, there will always be situations when they fail. In these cases, robust vision based systems are required. Traffic signs are designed to have distinct colored fields separated by sharp boundaries. We propose to use locally segmented contours combined with an implicit star-shaped object model as prototypes for the different sign classes. The contours are described by Fourier descriptors. Matching of a query image to the sign prototype database is done by exhaustive search. This is done efficiently by using the correlation based matching scheme for Fourier descriptors and a fast cascaded matching scheme for enforcing the spatial requirements. We demonstrated on a publicly available database state of the art performance.

Original Publication: Fredrik Larsson and Michael Felsberg, Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition, SCIA konferens, 23-27 May 2011, Ystad Sweden, 2011, Lecture Notes in Computer Science, Image Analysis, 238-249. http://dx.doi.org/10.1007/978-3-642-21227-7_23 Copyright: Springer Document type: Part of book or chapter of book

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The different versions of the original document can be found in:

• http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69521

• https://link.springer.com/content/pdf/10.1007%2F978-3-642-21227-7_23.pdf