Multiple thresholding and subspace based approach for detection and recognition of traffic sign

Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

[1]  Johannes Stallkamp,et al.  Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition , 2012, Neural Networks.

[2]  Alastair R. Allen,et al.  Using self-organising maps in the detection and recognition of road signs , 2009, Image Vis. Comput..

[3]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[4]  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).

[5]  A. H. Mazinan,et al.  Providing an efficient intelligent transportation system through detection, tracking and recognition of the region of interest in traffic signs by using non-linear SVM classifier in line with histogram oriented gradient and Kalman filter approach , 2014 .

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

[7]  Andrew Beng Jin Teoh,et al.  Neighbourhood preserving discriminant embedding in face recognition , 2009, J. Vis. Commun. Image Represent..

[8]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[9]  Tamitza Toroyan,et al.  Global status report on road safety , 2009, Injury Prevention.

[10]  Guanghui Ren,et al.  Hole-based traffic sign detection method for traffic signs with red rim , 2013, The Visual Computer.

[11]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[12]  Xiaohui Liu,et al.  Real-time traffic sign recognition from video by class-specific discriminative features , 2010, Pattern Recognit..

[13]  Sancho Salcedo-Sanz,et al.  A decision support system for the automatic management of keep-clear signs based on support vector machines and geographic information systems , 2010, Expert Syst. Appl..

[14]  Kun Zhou,et al.  Locality Sensitive Discriminant Analysis , 2007, IJCAI.

[15]  Luc Van Gool,et al.  Traffic sign recognition — How far are we from the solution? , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[16]  Mária Gamcová,et al.  Algorithm of traffic signs recognition based on the rapid transform , 2012, Central European Journal of Computer Science.

[17]  Kyung-Joong Kim,et al.  Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detection , 2013, Expert Syst. Appl..

[18]  U. Raghavendra,et al.  Anchor-diagonal-based shape adaptive local support region for efficient stereo matching , 2015, Signal Image Video Process..

[19]  Luc Van Gool,et al.  Multi-view traffic sign detection, recognition, and 3D localisation , 2014, Machine Vision and Applications.

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

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

[22]  Anjan Gudigar,et al.  Kernel Based Automatic Traffic Sign Detection and Recognition Using SVM , 2012, ICECCS 2012.

[23]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

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

[25]  Nasser Kehtarnavaz,et al.  Traffic sign recognition in noisy outdoor scenes , 1995, Proceedings of the Intelligent Vehicles '95. Symposium.

[26]  Danwei Wang,et al.  Application of BW-ELM model on traffic sign recognition , 2014, Neurocomputing.

[27]  Majid Mirmehdi,et al.  Real-Time Detection and Recognition of Road Traffic Signs , 2012, IEEE Transactions on Intelligent Transportation Systems.

[28]  Mahdi Yaghoobi,et al.  A new approach in road sign recognition based on fast fractal coding , 2011, Neural Computing and Applications.

[29]  Michael H. F. Wilkinson,et al.  Morphological hat-transform scale spaces and their use in pattern classification , 2004, Pattern Recognit..

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

[31]  Francisco López-Ferreras,et al.  Traffic sign shape classification and localization based on the normalized FFT of the signature of blobs and 2D homographies , 2008, Signal Processing.

[32]  Jürgen Schmidhuber,et al.  A committee of neural networks for traffic sign classification , 2011, The 2011 International Joint Conference on Neural Networks.

[33]  Ke Lu,et al.  Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition , 2012, IEEE Transactions on Intelligent Transportation Systems.

[34]  Fuchun Sun,et al.  Traffic sign recognition using group sparse coding , 2014, Inf. Sci..

[35]  Alexandre Bernardino,et al.  A review of log-polar imaging for visual perception in robotics , 2010, Robotics and Autonomous Systems.

[36]  Sergio Escalera,et al.  Traffic sign recognition system with β -correction , 2010, Machine Vision and Applications.

[37]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[38]  H. Fleyeh,et al.  Eigen-based traffic sign recognition , 2011 .

[39]  Fatin Zaklouta,et al.  Real-time traffic sign recognition in three stages , 2014, Robotics Auton. Syst..

[40]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[41]  Yuzo Hirai,et al.  Real-Time Recognition of Blue Traffic Signs Designating Directions , 2010, Int. J. Intell. Transp. Syst. Res..

[42]  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..

[43]  Hong Liu,et al.  Coarse to fine K nearest neighbor classifier , 2013, Pattern Recognit. Lett..

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

[45]  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).

[46]  Christophe Cudel,et al.  Triangular traffic signs detection based on RSLD algorithm , 2013, Machine Vision and Applications.

[47]  Yok-Yen Nguwi,et al.  Emergent self-organizing feature map for recognizing road sign images , 2010, Neural Computing and Applications.

[48]  Yong Xu,et al.  Locality and similarity preserving embedding for feature selection , 2014, Neurocomputing.

[49]  Yann LeCun,et al.  Traffic sign recognition with multi-scale Convolutional Networks , 2011, The 2011 International Joint Conference on Neural Networks.

[50]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[51]  Anjan Gudigar,et al.  A review on automatic detection and recognition of traffic sign , 2014, Multimedia Tools and Applications.

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

[53]  Kamel Besbes,et al.  Efficient algorithm for automatic road sign recognition and its hardware implementation , 2013, Journal of Real-Time Image Processing.

[54]  Michael H. F. Wilkinson,et al.  Morphological hat-transform scale spaces and their use in texture classification , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).