Multi-view traffic sign detection, recognition, and 3D localisation

Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. A van with 8 roof-mounted cameras drove through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs. The task is challenging, as illumination conditions change regularly, occlusions are frequent, 3D positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis.

[1]  A. Broggi,et al.  Real Time Road Signs Recognition , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[2]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[4]  Bernt Schiele,et al.  A Dynamic Conditional Random Field Model for Joint Labeling of Object and Scene Classes , 2008, ECCV.

[5]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[6]  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).

[7]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

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

[9]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[10]  Vladimir G. Kim,et al.  Shape-based recognition of 3D point clouds in urban environments , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[11]  Martial Hebert,et al.  Directional Associative Markov Network for 3-D Point Cloud Classification , 2008 .

[12]  Luc Van Gool,et al.  Integrating Object Detection with 3D Tracking Towards a Better Driver Assistance System , 2010, 2010 20th International Conference on Pattern Recognition.

[13]  Luc Van Gool,et al.  3D Urban Scene Modeling Integrating Recognition and Reconstruction , 2008, International Journal of Computer Vision.

[14]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Stefan Roth,et al.  People-tracking-by-detection and people-detection-by-tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  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..

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

[18]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[19]  C. Nunn,et al.  A novel region of interest selection approach for traffic sign recognition based on 3D modelling , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[20]  David A. McAllester,et al.  Cascade object detection with deformable part models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[22]  Roberto Cipolla,et al.  Segmentation and Recognition Using Structure from Motion Point Clouds , 2008, ECCV.

[23]  S. Lafuente-Arroyo,et al.  Traffic sign shape classification evaluation I: SVM using distance to borders , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[24]  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.

[25]  Fatih Murat Porikli,et al.  Machine Vision and Applications DOI 10.1007/s00138-009-0231-x ORIGINAL PAPER In-vehicle camera traffic sign detection and recognition , 2022 .

[26]  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).

[27]  Luc Van Gool,et al.  Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  N. Pettersson,et al.  The histogram feature - a resource-efficient Weak Classifier , 2008, 2008 IEEE Intelligent Vehicles Symposium.

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

[30]  Ruzena Bajcsy,et al.  Segmentation of range images as the search for geometric parametric models , 1995, International Journal of Computer Vision.

[31]  Alexei A. Efros,et al.  Putting Objects in Perspective , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[32]  Petr Doubek,et al.  Mobile Mapping of Vertical Traffic Infrastructure , 2008 .