Detection and Recognition of U.S. Warning Signs on Curves

Traffic sign detection and recognition has been studied in multiple areas including civil and transportation engineering, automated driving, and computer vision. However, previous work has devoted relatively less attention to U.S. signs. Among all types of U.S. signs, warning signs are the most crucial to road safety. In this work, we propose a customized detection and recognition method for U.S. warning signs that does not require training. For detection, preprocessing with a two-layer HSV-B filter and style transfer reduces color bias and a substantial number of false positives. We formulate the recognition process as a template-matching problem in which pre-trained deep networks serve as feature extractors and we use the cosine distance as the distance metric. Best results on selected images from Georgia State Route 2 achieve above 90% precision and recall in detection and 92.6% accuracy in recognition. Further tests on large datasets demonstrate that the proposed method is promising, and it can support transportation agencies in the management of their warning sign assets.

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

[2]  Roberto Brunelli,et al.  Template Matching Techniques in Computer Vision: Theory and Practice , 2009 .

[3]  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..

[4]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Zhaozheng Hu,et al.  Generalized Image Recognition Algorithm for Sign Inventory , 2011 .

[6]  Ali Behloul,et al.  An overview of traffic sign detection and classification methods , 2017, International Journal of Multimedia Information Retrieval.

[7]  Reinhard Klette,et al.  General traffic sign recognition by feature matching , 2009, 2009 24th International Conference Image and Vision Computing New Zealand.

[8]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[10]  Cui-Hua Li,et al.  Unifying visual saliency with HOG feature learning for traffic sign detection , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[11]  Majid Mirmehdi,et al.  Traffic sign recognition using MSER and Random Forests , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[12]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[13]  Baoli Li,et al.  Traffic-Sign Detection and Classification in the Wild , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  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.

[15]  Bin Fan,et al.  Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network , 2018, IEEE Transactions on Intelligent Transportation Systems.

[16]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  Seyed-Mohsen Moosavi-Dezfooli,et al.  Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Atul Prakash,et al.  Robust Physical-World Attacks on Machine Learning Models , 2017, ArXiv.

[19]  Mani Golparvar-Fard,et al.  Evaluation of Multiclass Traffic Sign Detection and Classification Methods for U.S. Roadway Asset Inventory Management , 2016, J. Comput. Civ. Eng..

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

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  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).

[23]  Andreas Møgelmose,et al.  Detection of U.S. Traffic Signs , 2015, IEEE Transactions on Intelligent Transportation Systems.

[24]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[25]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[26]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[27]  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..

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

[29]  Zhaohua Wang,et al.  Generalized Traffic Sign Detection Model for Developing a Sign Inventory , 2009 .

[30]  Klaus Zimmermann,et al.  Towards reliable traffic sign recognition , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[31]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

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

[33]  Salina Abdul Samad,et al.  Comparative Survey on Traffic Sign Detection and Recognition: a Review , 2015 .

[34]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.