Color Features for Vision-based Traffic Sign Candidate Detection

A common approach for traffic sign detection and recognition algorithms is to use shape based and in addition color features. Especially to distinguish between speed-limitand end-of-speed-limit-signs the usage of color information can be helpful as the outer border of speed-signs is in a forceful red. In this paper the focus is faced on color features of speed-limit and no-overtaking signs. The apparent color in the captured image is varying very much due to illumination conditions, sign surface condition and viewing angle. Therefore the color distribution in the HSV color space of a sufficient amount of signs at different illumination conditions and aging has been collected, examined, and a matching mathematical model is developed to describe the subregion in the according color space. Once the color region of traffic signs is known, two kinds of traffic sign segmentation algorithms are developed and evaluated with the explicit focus only on color features to preselect subregions in the image where (red bordered) traffic signs are likely to be.

[1]  S. Shevell.  The Science of Color , 2003 .

[2]  A. Zelinsky,et al.  Real-time radial symmetry for speed sign detection , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[3]  G.K. Siogkas,et al.  Detection, Tracking and Classification of Road Signs in Adverse Conditions , 2006, MELECON 2006 - 2006 IEEE Mediterranean Electrotechnical Conference.

[4]  Lutz Priese,et al.  Fast and Robust Segmentation of Natural Color Scenes , 1998, ACCV.

[5]  B. Johansson,et al.  Road Sign Recognition from a Moving Vehicle , 2002 .

[6]  H. Fleyeh.  Shadow And Highlight Invariant Colour Segmentation Algorithm For Traffic Signs , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[7]  Lutz Priese,et al.  New results on traffic sign recognition , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[8]  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..

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

[10]  J. Torresen,et al.  Efficient recognition of speed limit signs , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[11]  Dariu Gavrila,et al.  Traffic Sign Recognition Revisited , 1999, DAGM-Symposium.

[12]  Filippo Sorbello,et al.  A neural network based automatic road signs recognizer , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[13]  Lutz Priese,et al.  Traffic Sign Recognition Based On Color Image Evaluationion , 1993, Proceedings of the Intelligent Vehicles '93 Symposium.

[14]  N. Lazarevic-McManus,et al.  Performance evaluation in visual surveillance using the F-measure , 2006, VSSN '06.

[15]  M. Benallal,et al.  Real-time color segmentation of road signs , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

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