Robust traffic sign detection using fuzzy shape recognizer

A novel fuzzy approach for the detection of traffic signs in natural environments is presented. More than 3000 road images were collected under different weather conditions by a digital camera, and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by the hue and saturation thresholds. A symmetrical detector was used to extract the local features of the regions of interest (ROI), and the shape of ROI was determined by a fuzzy shape recognizer which invoked a set of fuzzy rules. The experimental results show that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur.

[1]  Koichi Yamada,et al.  Traffic Sign Classification Using Ring Partitioned Method , 2005, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[2]  Miguel Ángel Sotelo,et al.  Fast traffic sign detection and recognition under changing lighting conditions , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[3]  Hasan Fleyeh.  A Novel Fuzzy Approach for Shape Determination of Traffic Signs , 2005, IICAI.

[4]  Sei-Wang Chen,et al.  An automatic road sign recognition system based on a computational model of human recognition processing , 2004, Comput. Vis. Image Underst..

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

[6]  Kenji Suzuki,et al.  Linear-time connected-component labeling based on sequential local operations , 2003, Comput. Vis. Image Underst..

[7]  Mark de Berg,et al.  Computational geometry: algorithms and applications , 1997 .

[8]  Luke Fletcher,et al.  Real-Time Speed Sign Detection Using the Radial Symmetry Detector , 2008, IEEE Transactions on Intelligent Transportation Systems.

[9]  Arturo de la Escalera,et al.  Traffic sign recognition and analysis for intelligent vehicles , 2003, Image Vis. Comput..

[10]  Boguslaw Cyganek,et al.  Soft System for Road Sign Detection , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

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

[12]  Giovanni Pilato,et al.  Road signs recognition using a dynamic pixel aggregation technique in the HSV color space , 2001, Proceedings 11th International Conference on Image Analysis and Processing.

[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]  Alexander Zelinsky,et al.  Vision In and Out of Vehicles: Integrated Driver and Road Scene Monitoring , 2004, Int. J. Robotics Res..

[15]  Christof Koch,et al.  Toward color image segmentation in analog VLSI: Algorithm and hardware , 1994, International Journal of Computer Vision.

[16]  Chung-Lin Huang,et al.  Road sign detection and recognition using matching pursuit method , 2001, Image Vis. Comput..

[17]  Bruce A. Draper,et al.  Color recognition in outdoor images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).