Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition

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.

[1]  Gösta H. Granlund,et al.  Fourier Preprocessing for Hand Print Character Recognition , 1972, IEEE Transactions on Computers.

[2]  Roberto Moreno-Díaz,et al.  Computer Aided Systems Theory - EUROCAST 2005, 10th International Conference on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 7-11, 2005, Revised Selected Papers , 2005, EUROCAST.

[3]  Sei-ichiro Kamata,et al.  Automatic road sign detection method based on Color Barycenters Hexagon model , 2008, 2008 19th International Conference on Pattern Recognition.

[4]  King-Sun Fu,et al.  Shape Discrimination Using Fourier Descriptors , 1977, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[7]  Yok-Yen Nguwi,et al.  Detection and classification of road signs in natural environments , 2008, Neural Computing and Applications.

[8]  木村 充 A.Papoulis: The Fourier Integral and its Applications. McGraw-Hill, New York 1962, 306頁, 15×23cm, $12.00. , 1963 .

[9]  Francisco López-Ferreras,et al.  Road-Sign Detection and Recognition Based on Support Vector Machines , 2007, IEEE Transactions on Intelligent Transportation Systems.

[10]  A.V. Oppenheim,et al.  The importance of phase in signals , 1980, Proceedings of the IEEE.

[11]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[12]  F. Larsson,et al.  Correlating fourier descriptors of local patches for road sign recognition , 2011 .

[13]  Miguel Ángel Sotelo,et al.  Fast Road Sign Detection Using Hough Transform for Assisted Driving of Road Vehicles , 2005, EUROCAST.

[14]  H. Fleyeh,et al.  Invariant Road Sign Recognition with Fuzzy ARTMAP and Zernike Moments , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[15]  Charles R. Giardina,et al.  Elliptic Fourier features of a closed contour , 1982, Comput. Graph. Image Process..

[16]  A. Papoulis,et al.  The Fourier Integral and Its Applications , 1963 .