Detection, Tracking and Classification of Road Signs in Adverse Conditions

In this paper a complete automatic system for road-sign detection, tracking, and classification is presented and evaluated. The processing of video frames in the L*a*b color space improves significantly the sign detection rate by processing the same frame in different normalized color spaces. The tracking module reduces significantly the processing time by transferring the sign detection information in the next frames and processing different radii signs in parallel. The proposed system is evaluated in normal, raining and night driving conditions. In a total number of 266 road-sign recordings, the complete system track and recognize successfully 216. The main source of system fault appears in city night driving due to the presence of a great number of light sources

[1]  S. Escalera,et al.  Fast greyscale road sign model matching and recognition , 2004 .

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

[3]  Chung-Lin Huang,et al.  Road sign detection and recognition using matching pursuit method , 2001, Image Vis. Comput..

[4]  T. Asakura,et al.  A study on traffic sign recognition in scene image using genetic algorithms and neural networks , 1996, Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation.

[5]  José Manuel Pastor,et al.  Visual sign information extraction and identification by deformable models for intelligent vehicles , 2004, IEEE Transactions on Intelligent Transportation Systems.

[6]  Fabrice Heitz,et al.  Unsupervised statistical detection of changing objects in camera-in-motion video , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  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).

[8]  Sei-Wang Chen,et al.  An automatic road sign recognition system based on a computational model of human recognition processing , 2004, Comput. Vis. Image Underst..

[9]  Jordi Vitrià,et al.  Fast Traffic Sign Detection on greyscale images , 2004 .

[10]  B. Ulmer,et al.  Shape Classification for Traffic Sign Recognition , 1993 .

[11]  Bärbel Mertsching,et al.  Parallel Evaluation of Hierarchical Image Databases , 1995, J. Parallel Distributed Comput..

[12]  Nasser Kehtarnavaz,et al.  An invariant traffic sign recognition system based on sequential color processing and geometrical transformation , 1994, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation.

[13]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[14]  J. M. Armingol,et al.  TRAFFIC SIGN DETECTION FOR DRIVER SUPPORT SYSTEMS , 2022 .

[15]  Luis Moreno,et al.  Road traffic sign detection and classification , 1997, IEEE Trans. Ind. Electron..

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

[17]  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..

[18]  Arturo de la Escalera,et al.  Traffic sign recognition and analysis for intelligent vehicles , 2003, Image Vis. Comput..

[19]  Bärbel Mertsching,et al.  Analysis of Traffic Scenes by Using the Hierarchical Structure Code , 1993 .

[20]  Alexander Zelinsky,et al.  Fast Radial Symmetry for Detecting Points of Interest , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Marco Campani,et al.  Robust method for road sign detection and recognition , 1996, Image Vis. Comput..