COLOR FEATURES FOR VISION-BASED TRAFFIC SIGN CANDIDATE DETECTION

Steffen Görmer, Anton Kummert, Stefan Müller-Schneiders

2009

Abstract

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-limit and 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.

References

  1. Gavrila, D.M. (1999). ”Traffic Sign Recognition Revisited”. In: Proc. of the 21st DAGM Symposium für Mustererkennung, pp. 86-93, Bonn, Germany
  2. Barnes, N., Zelinsky, A. (2004). ”Real-time Radial Symmetry for Speed Sign Detection”. In: IEEE Proc. of the Intelligent Vehicles Symposium 04, pp. 566-571, Parma, Italy
  3. Bahlmann, C., Zhu, Y., Ramesh, V., Pellkofer, M., Koehler, T. (2005). ”A System for Traffic Sign Detection, Tracking, and Recognition Using Color, Shape, and Motion Information”. In: IEEE Proc. of the Intelligent Vehicles Symposium 05, pp. 255-260
  4. de la Escalera, A., Armingol, J., Mata, M. (2003). ”Traffic sign recognition and analysis for intelligent vehicles”. In: Image and Vision Comput. Vol. 21, pp. 247-258
  5. Fang, C., Chen, S., Fuh, C. (2003). ”Road-sign detection and tracking”, In: IEEE Transactions on Vehicular Technology vol. 52, No. 5, pp. 1329-1341
  6. Siogkas, G. K., Dermatas, E. S. (2006). ”Detection, Tracking and Classification of Road Signs in Adverse Conditions”. In: IEEE MELECON 2006, Malaga, Spain
  7. Torresen, J., Bakke, J. W., Sekanina, L. (2004). ”Efficient Recognition of Speed Limit Signs”. In: IEEE Intelligent Transportation Systems Conference, Washington D.C., USA.
  8. Johansson, B. (2002) ”Road Sign Recognition from a Moving Vehicle”. Master's Thesis Report No. 56, University of Uppsala, Sweden
  9. Priese, L., Rehrmann, V., Schian, R., Lakmann, R. (1993). ”Traffic Sign Recognition Based on Color Image Evaluation”. In: IEEE Proc. of the Intelligent Vehicles Symposium, pp. 95-100
  10. Priese, L., Klieber, J., Lakmann, R., Rehrmann, V., Schian, R. (1994). ”New Results on Traffic Sign Recognition”. In: IEEE Proc. of the Intelligent Vehicles Symposium, pp. 249-254, Paris
  11. Priese, L., Rehrmann, V. (1998). ”Fast and Robust Segmentation of Natural Color Scenes”. In: 3rd Asian Conference on Computer Vision, pp. 598-606
  12. Fleyeh, H. (2006). ”Shadow And Highlight Invariant Colour Segmentation For Traffic Signs”. In: IEEE Conf. on Cybernetics and Intelligent Systems, Thailand
  13. Gonzales, R. C., Woods, R. E. (2002) ”Digital Image Processing”, 2nd ed.: Prentice Hall
  14. Shevell, S. K. (2003). ”The Science of Color”. 2nd ed.: Optical Society of America
  15. Bénallal, M., Meunier, J. (2003). ”Real-time color segmentation of road signs”. In: IEEE Canadian Conf. on Electrical and Computer Engineering, Montréal
  16. Vitabile, S., Gentile, A., Sorbello, F. (2002). ”A neural network based automatic road sign recognizer”. In: Intern. Joint Conf. on Neural Networks, Honolulu
  17. Lazarevic-McManus, N., Renno, J., Jones, G.A. (2006). ”Performance evaluation in visual surveillance using the F-measure”. In: Proc. of the 4th International Workshop On Video Surveillance And Sensor Networks, pp. 45-52, ACM Press, New York
Download


Paper Citation


in Harvard Style

Görmer S., Kummert A. and Müller-Schneiders S. (2009). COLOR FEATURES FOR VISION-BASED TRAFFIC SIGN CANDIDATE DETECTION . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 107-113. DOI: 10.5220/0001746101070113


in Bibtex Style

@conference{visapp09,
author={Steffen Görmer and Anton Kummert and Stefan Müller-Schneiders},
title={COLOR FEATURES FOR VISION-BASED TRAFFIC SIGN CANDIDATE DETECTION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={107-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001746101070113},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - COLOR FEATURES FOR VISION-BASED TRAFFIC SIGN CANDIDATE DETECTION
SN - 978-989-8111-69-2
AU - Görmer S.
AU - Kummert A.
AU - Müller-Schneiders S.
PY - 2009
SP - 107
EP - 113
DO - 10.5220/0001746101070113