An Approach to the Recognition of Informational Traffic Signs Based on 2-D Homography and SVMs

A fast method for the recognition and classification of informational traffic signs is presented in this paper. The aim is to provide an efficient framework which could be easily used in inventory and guidance systems. The process consists of several steps which include image segmentation, sign detection and reorientation, and finally traffic sign recognition. In a first stage, a static HSI colour segmentation is performed so that possible traffic signs can be easily isolated from the rest of the scene; secondly, shape classification is carried out so as to detect square blobs from the segmented image; next, each object is reoriented through the use of a homography transformation matrix and its potential axial deformation is corrected. Finally a recursive adaptive segmentation and a SVM-based recognition framework allow us to extract each possible pictogram, icon or symbol and classify the type of the traffic sign via a voting-scheme.

[1]  Yoshiaki Shirai,et al.  An active vision system for real-time traffic sign recognition , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[2]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[3]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[4]  José Manuel Pastor,et al.  Visual sign information extraction and identification by deformable models for intelligent vehicles , 2004, IEEE Transactions on Intelligent Transportation Systems.

[5]  Xilin Chen,et al.  Automatic detection and recognition of signs from natural scenes , 2004, IEEE Transactions on Image Processing.

[6]  Christine Connolly,et al.  A study of efficiency and accuracy in the transformation from RGB to CIELAB color space , 1997, IEEE Trans. Image Process..

[7]  S. Lafuente-Arroyo,et al.  Traffic sign shape classification evaluation I: SVM using distance to borders , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[8]  Thomas S. Huang,et al.  Image processing , 1971 .

[10]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[11]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[12]  Mark D. Fairchild,et al.  A top down description of S-CIELAB and CIEDE2000 , 2003 .

[13]  S. Lafuente-Arroyo,et al.  Traffic sign shape classification evaluation. Part II. FFT applied to the signature of blobs , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[14]  Xilin Chen,et al.  Detection of text on road signs from video , 2005, IEEE Trans. Intell. Transp. Syst..

[15]  Pavel Pudil,et al.  Road sign classification using Laplace kernel classifier , 2000, Pattern Recognit. Lett..

[16]  H. Hama,et al.  Robust road sign recognition using standard deviation , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

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

[18]  Ismail Haritaoglu,et al.  Real time image enhancement and segmentation for sign/text detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).