Optimized Method for Iranian Road Signs Detection and recognition system

Road sign recognition is one of the core technologies in Intelligent Transport Systems. In the current study, a robust and real-time method is presented to identify and detect the roads speed signs in road image in different situations. In our proposed method, first, the connected components are created in the main image using the edge detection and mathematical morphology and the location of the road signs extracted by the geometric and color data; then the letters are segmented and recognized by Multiclass Support Vector Machine (SVMs) classifiers. Regarding that the geometric and color features ate properly used in detection the location of the road signs, so it is not sensitive to the distance and noise and has higher speed and efficiency. In the result part, the proposed approach is applied on Iranian road speed sign database and the detection and recognition accuracy rate achieved 98.66% and 100% respectively..

[1]  H. R. Mamatha,et al.  Kannada Characters Recognition - A Novel Approach Using Image Zoning and Run Length Count , 2011 .

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

[3]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[4]  Mark Dougherty,et al.  Road and traffic sign detection and recognition , 2005 .

[5]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  A. Pinz,et al.  Traffic sign detection as a component of an automated traffic infrastructure inventory system ∗ , 2009 .

[7]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Wen-Jia Kuo,et al.  Two-Stage Road Sign Detection and Recognition , 2007, 2007 IEEE International Conference on Multimedia and Expo.

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

[10]  Reza Azad,et al.  Optimized Method for Real-Time Face Recognition System Based on PCA and Multiclass Support Vector Machine , 2013 .

[11]  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..

[12]  Werner Frei,et al.  Fast Boundary Detection: A Generalization and a New Algorithm , 1977, IEEE Transactions on Computers.

[13]  Francisco López-Ferreras,et al.  Complexity reduction in Neural Networks applied to traffic sign recognition tasks , 2005, 2005 13th European Signal Processing Conference.

[14]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[15]  Nasser Kehtarnavaz,et al.  An invariant traffic sign recognition system based on sequential color processing and geometrical transformation , 1994, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation.

[16]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jesper Salomon,et al.  Support Vector Machines for Phoneme Classification , 2001 .

[18]  M. Benallal,et al.  Real-time color segmentation of road signs , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[19]  Sei-Wang Chen,et al.  Road-sign detection and tracking , 2003, IEEE Trans. Veh. Technol..

[20]  Paulo Lobato Correia,et al.  Automatic Detection and Classification of Traffic Signs , 2007, Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07).

[21]  Marco Campani,et al.  Robust method for road sign detection and recognition , 1996, Image Vis. Comput..

[22]  Lorenzo Bruzzone,et al.  Support vector machines for classification of hyperspectral remote-sensing images , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[23]  Tai-Yue Wang,et al.  Fuzzy support vector machine for multi-class text categorization , 2007, Inf. Process. Manag..

[24]  Dariu Gavrila,et al.  Traffic Sign Recognition Revisited , 1999, DAGM-Symposium.

[25]  Saturnino Maldonado-Bascón,et al.  Knowledge Modeling for the Traffic Sign Recognition Task , 2005, IWINAC.

[26]  B. Philip,et al.  A Road Traffic Signal Recognition System Based on Template Matching Employing Tree Classifier , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[27]  Gareth Blake Loy,et al.  Fast shape-based road sign detection for a driver assistance system , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[28]  Nello Cristianini,et al.  Large Margin DAGs for Multiclass Classification , 1999, NIPS.

[29]  Xiaohui Liu,et al.  Detection, Tracking and Recognition of Traffic Signs from Video Input , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[30]  Bin Ran,et al.  Vision-Based Stop Sign Detection and Recognition System for Intelligent Vehicles , 2001 .

[31]  Xiaohong W. Gao,et al.  Recognition of traffic signs based on their colour and shape features extracted using human vision models , 2006, J. Vis. Commun. Image Represent..

[32]  Reza Azad,et al.  Recognition of Handwritten Persian/Arabic Numerals Based on Robust Feature Set and K-NN Classifier , 2014, ArXiv.

[33]  Nasser Kehtarnavaz,et al.  A real-time histographic approach to road sign recognition , 1996, Proceeding of Southwest Symposium on Image Analysis and Interpretation.

[34]  Manuel Rosa-Zurera,et al.  Multilayer Perceptrons Applied to Traffic Sign Recognition Tasks , 2005, IWANN.

[35]  A. Broggi,et al.  Real Time Road Signs Recognition , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[36]  Reza Azad,et al.  A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition , 2014, ArXiv.

[37]  N. Kamaraj,et al.  Evolving GA Classifier for Breaking the Steganographic Utilities: Stools, Steganos and Jsteg , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[38]  Visvanathan Ramesh,et al.  A system for traffic sign detection, tracking, and recognition using color, shape, and motion information , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

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