Towards Real-Time Traffic Sign Detection and Classification

Traffic sign recognition plays an important role in driver assistant systems and intelligent autonomous vehicles. Its real-time performance is highly desirable in addition to its recognition performance. This paper aims to deal with real-time traffic sign recognition, i.e., localizing what type of traffic sign appears in which area of an input image at a fast processing time. To achieve this goal, we first propose an extremely fast detection module, which is 20 times faster than the existing best detection module. Our detection module is based on traffic sign proposal extraction and classification built upon a color probability model and a color HOG. Then, we harvest from a convolutional neural network to further classify the detected signs into their subclasses within each superclass. Experimental results on both German and Chinese roads show that both our detection and classification methods achieve comparable performance with the state-of-the-art methods, with significantly improved computational efficiency.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Xiaolin Hu,et al.  Traffic sign detection based on convolutional neural networks , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[3]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[4]  Fatin Zaklouta,et al.  Traffic sign classification using K-d trees and Random Forests , 2011, The 2011 International Joint Conference on Neural Networks.

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

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

[7]  Yi Yang,et al.  Real-Time Traffic Sign Detection via Color Probability Model and Integral Channel Features , 2014, CCPR.

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

[9]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

[10]  Nozha Boujemaa,et al.  Color texture classification by normalized color space representation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[12]  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).

[13]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[14]  Johannes Stallkamp,et al.  The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.

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

[16]  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).

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

[18]  Zhilu Wu,et al.  A hierarchical method for traffic sign classification with support vector machines , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[19]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[20]  Federico Tombari,et al.  A traffic sign detection pipeline based on interest region extraction , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[21]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[23]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[24]  Thomas B. Moeslund,et al.  Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey , 2012, IEEE Transactions on Intelligent Transportation Systems.

[25]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[26]  许华荣,et al.  Towards Real-Time Traffic Sign Detection and Classification , 2016 .

[27]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[28]  Changshui Zhang,et al.  Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks , 2014, IEEE Transactions on Intelligent Transportation Systems.