Traffic sign detection for U.S. roads: Remaining challenges and a case for tracking

Traffic sign detection is crucial in intelligent vehicles, no matter if one's objective is to develop Advanced Driver Assistance Systems or autonomous cars. Recent advances in traffic sign detection, especially the great effort put into the competition German Traffic Sign Detection Benchmark, have given rise to very reliable detection systems when tested on European signs. The U.S., however, has a rather different approach to traffic sign design. This paper evaluates whether a current state-of-the-art traffic sign detector is useful for American signs. We find that for colorful, distinctively shaped signs, Integral Channel Features work well, but it fails on the large superclass of speed limit signs and similar designs. We also introduce an extension to the largest public dataset of American signs, the LISA Traffic Sign Dataset, and present an evaluation of tracking in the context of sign detection. We show that tracking essentially suppresses all false positives in our test set, and argue that in order to be useful for higher level analysis, any traffic sign detection system should contain tracking.

[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]  Lars Petersson,et al.  Large scale sign detection using HOG feature variants , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[3]  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..

[4]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[5]  Xiaohui Liu,et al.  Towards Real-Time Traffic Sign Recognition by Class-Specific Discriminative Features , 2007, BMVC.

[6]  Ulrich Kressel,et al.  Confidence Measurements for Adaptive Bayes Decision Classifier Cascades and Their Application to US Speed Limit Detection , 2012, DAGM/OAGM Symposium.

[7]  Mohan M. Trivedi,et al.  Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation , 2013, IEEE Transactions on Intelligent Transportation Systems.

[8]  Mohan M. Trivedi,et al.  Drive Analysis Using Vehicle Dynamics and Vision-Based Lane Semantics , 2015, IEEE Transactions on Intelligent Transportation Systems.

[9]  Christophe Cudel,et al.  Coupled detection, association and tracking for Traffic Sign Recognition , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[10]  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).

[11]  Eriksson,et al.  [IEEE Conference on Intelligent Transportation Systems - Boston, MA, USA (9-12 Nov. 1997)] Proceedings of Conference on Intelligent Transportation Systems - Eye-tracking for detection of driver fatigue , 1997 .

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

[13]  Nick Barnes,et al.  Real-Time Regular Polygonal Sign Detection , 2005, FSR.

[14]  Mohan M. Trivedi,et al.  This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 Integrated Lane and Vehicle Detection, Localization, , 2022 .

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

[16]  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.

[17]  Shu Wang,et al.  A new edge feature for head-shoulder detection , 2013, 2013 IEEE International Conference on Image Processing.

[18]  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.

[19]  Mohan M. Trivedi,et al.  Robust classification and tracking of vehicles in traffic video streams , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[20]  Peter H. N. de With,et al.  Robust classification system with reliability prediction for semi-automatic traffic-sign inventory systems , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

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

[22]  Ayman Mansour,et al.  Speed Sign Recognition using Shape-based Features , 2013 .

[23]  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).

[24]  Mohan M. Trivedi,et al.  Fast and Robust Object Detection Using Visual Subcategories , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[25]  A. Herbin,et al.  Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[26]  Mohan M. Trivedi,et al.  Learning to detect traffic signs: Comparative evaluation of synthetic and real-world datasets , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[27]  C. Bahlmann,et al.  Real-time recognition of U.S. speed signs , 2008, 2008 IEEE Intelligent Vehicles Symposium.