Combining traffic sign detection with 3D tracking towards better driver assistance

We briefly review the advances in driver assistance systems and present a realtime version that integrates single view detection with region-based 3D tracking of traffic signs. The system has a typical pipeline: detection and recognition of traffic signs in independent frames, followed by tracking for temporal integration. The detection process finds an optimal set of candidates and is accelerated using AdaBoost cascades. A hierarchy of SVMs handles the recognition of traffic sign types. The 2D detections are then employed in simultaneous 2D segmentation and 3D pose tracking, using the known 3D model of the recognized traffic sign. Thus, we achieve not only 2D tracking of the recognized traffic signs, but we also obtain 3D pose information, which we use to establish the relevance of the traffic sign to the driver. The performance of the system is demonstrated by tracking multiple road signs in real-world scenarios.

[1]  Bodo Rosenhahn,et al.  Region-Based Pose Tracking , 2007, IbPRIA.

[2]  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.

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

[4]  Giovanni Pilato,et al.  Road signs recognition using a dynamic pixel aggregation technique in the HSV color space , 2001, Proceedings 11th International Conference on Image Analysis and Processing.

[5]  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).

[6]  D. L. Kellmeyer,et al.  Detection of highway warning signs in natural video images using color image processing and neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

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

[8]  Bodo Rosenhahn,et al.  Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking , 2007, International Journal of Computer Vision.

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

[10]  Sei-Wang Chen,et al.  Road-sign detection and tracking , 2003, IEEE Trans. Veh. Technol..

[11]  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.

[12]  Dariu Gavrila,et al.  Real-time object detection for "smart" vehicles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Subhransu Maji,et al.  Fast and Accurate Digit Classification , 2009 .

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

[15]  Ian D. Reid,et al.  Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors , 2008, ECCV.

[16]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

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

[20]  Anton Kummert,et al.  3D Traffic Sign Tracking Using a Particle Filter , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

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

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

[23]  Jiawei Han,et al.  SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis , 2008, IEEE Transactions on Knowledge and Data Engineering.

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

[25]  Syed Omer Gilani,et al.  Road sign detection and recognition using fuzzy artmap: A case study swedish speed-limit signs , 2006, Artificial Intelligence and Soft Computing.

[26]  Gareth Blake Loy,et al.  Fast shape-based road sign detection for a driver assistance system , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[27]  M. Campani,et al.  Robust road sign detection and recognition from image sequences , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

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

[29]  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.

[30]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[31]  Ian D. Reid,et al.  PWP3D: Real-time Segmentation and Tracking of 3D Objects , 2009, BMVC.

[32]  Olac Fuentes,et al.  Color-Based Road Sign Detection and Tracking , 2007, ICIAR.