Traffic Sign Detection Based on Multiple Features Cooperation

A new method is presented to improve traffic sign detection with cooperation of color,shape and scale features,especially under conditions of color distortion,shape deformation and scale variance. Color enhancement maps are generated from traffic scene images. Regions of interest are then extracted from the color enhancement maps using multiple thresholds of color,chain codes of the curvature histograms of closed contours are calculated and scale normalized for the contours. The Support Vector Machine(SVM)classifier is applied to classify the chain codes of the extracted traffic signs and the template signs. Experimental results demonstrate that this method is capable of improving traffic sign detection,with low time complexity.

[1]  Xiaolin Hu,et al.  Traffic sign detection by ROI extraction and histogram features-based recognition , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[2]  Zhilu Wu,et al.  A robust, coarse-to-fine traffic sign detection method , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[3]  Saturnino Maldonado-Bascón,et al.  Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition , 2010, IEEE Transactions on Intelligent Transportation Systems.

[4]  Jesmin F. Khan,et al.  Image Segmentation and Shape Analysis for Road-Sign Detection , 2011, IEEE Transactions on Intelligent Transportation Systems.

[5]  Toshiaki Fujii,et al.  Traffic sign detection in dual-focal active camera system , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[6]  Luc Van Gool,et al.  Multi-view traffic sign detection, recognition, and 3D localisation , 2014, 2009 Workshop on Applications of Computer Vision (WACV).

[7]  Jordi Vitrià,et al.  Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification , 2009, IEEE Transactions on Intelligent Transportation Systems.

[8]  Baolin Sun,et al.  The Multiplicity of Intelligent Agent Based Healthcare Sensor Decision Network , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

[9]  Qingquan Li,et al.  Traffic sign detection and recognition for intelligent vehicle , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[10]  Xiaohong W. Gao,et al.  Colour Vision Model-Based Approach for Segmentation of Traffic Signs , 2008, EURASIP J. Image Video Process..

[11]  Michael Wooldridge,et al.  Introduction to Multi-Agent Systems , 2016 .

[12]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

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

[14]  Sebastian Houben,et al.  A single target voting scheme for traffic sign detection , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[15]  David Fernández Llorca,et al.  Robust traffic signs detection by means of vision and V2I communications , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[17]  Peter H. N. de With,et al.  Color exploitation in hog-based traffic sign detection , 2010, 2010 IEEE International Conference on Image Processing.

[18]  Johannes Stallkamp,et al.  Detection of traffic signs in real-world images: The German traffic sign detection benchmark , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).