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Color-Based Road Sign Detection and Tracking

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Image Analysis and Recognition (ICIAR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4633))

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Abstract

This paper describes a general framework for the detection and tracking of traffic and road signs from image sequences using only color information. The approach consists of two independent parts. In the first we use a set of Gaussian distributions that model each color for detecting road and traffic signs. In the second part we track the targets detected in the first step over time. Our approach is tested using image sequences with high clutter that contain targets with the presence of rotation and partial occlusion. Experimental results show that the proposed system detects on average 97% of the targets in the scene in near real-time with an average of 2 false detections per sequence.

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References

  1. Bahlmann, C., Zhu, Y., Ramesh, V., Pellkofer, M., Koehler, T.: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV 2005), IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  2. Ballard, D., Brown, C.: Computer Vision. Prentice-Hall, Englewood Cliffs, New Jersey (1982)

    Google Scholar 

  3. Benallal, M., Meunier, J.: Real-time color segmentation of road signs. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering (IEEE CCECE 2003), pp. 1823–1826 (2003)

    Google Scholar 

  4. Chutorian, M., Trivedi, M.M.: N-tree Disjoint-Set Forests for Maximally Stable Extremal Regions. In: Proceedings of the British Machine Vision Conference, Edinburgh, PA (September 2006)

    Google Scholar 

  5. de la Escalera, A., Moreno, L.: Road traffic sign detection and classification. IEEE Transactions on Industrial Electronics 44, 848–859 (1997)

    Article  Google Scholar 

  6. Fang, C.-Y., Chen, S.-W., Fuh, C.-S.: Road-sign detection and tracking. IEEE Transactions on Vehicular Technology 52(5), 1329–1341 (2003)

    Article  Google Scholar 

  7. Fleyeh, H., Dougherty, M.: Road and traffic sign detection and recognition. In: Proceedings of the 10th EWGT Meeting and 16th Mini-EURO Conference (2005)

    Google Scholar 

  8. Gavrila, D., Philomin, V.: Real-Time Object Detection for Smart Vehicles. In: Proceedings of International Conference on Computer Vision, pp. 87–93 (1999)

    Google Scholar 

  9. Gutiérrez, L.D.L., Robles, L.A.: Decision Fusion for Target Detection Using Multi-spectral Image Sequences from Moving Cameras. In: Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal, June 2005, pp. 720–727 (2005)

    Google Scholar 

  10. Isard, M., Blake, A.: Condensation – conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)

    Article  Google Scholar 

  11. Loy, G., Barnes, N.M.: Fast Shape-based Road Sign Detection for a Driver Assistance System. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 70–75. IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  12. Martínez, C., Fuentes, O.: Face Recognition using Unlabeled Data. Iberoamerican Journal of Computer Science Research 7, 123–129 (2003)

    Google Scholar 

  13. Shadeed, W.G., Abu-Al-Nadi, D.I., Mismar, M.J.: Proceedings of the 10th IEEE International Conference on Electronics, Circuits and Systems, pp. 890–893

    Google Scholar 

  14. Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-time Tracking. In: CVPR. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages II, pp. 246–252. IEEE Computer Society Press, Los Alamitos (1999)

    Google Scholar 

  15. Vezhnevets, V., Sazonov, V., Andreeva, A.: A survey on pixel-based skin color detection techniques. In: Proceedings of Graphicon (2003)

    Google Scholar 

  16. Zadeh, M., Kasvand, T., Suen, C.: Localization and recognition of traffic signs for automated vehicle control systems. In: Proceedings of the Conference on Intelligent Transportation Systems, Pittsburgh, PA (October 1997)

    Google Scholar 

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Authors and Affiliations

  1. Computer Science Department University of Texas, El Paso 79902, USA

    Luis David Lopez & Olac Fuentes

Authors
  1. Luis David Lopez
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  2. Olac Fuentes
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Mohamed Kamel Aurélio Campilho

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© 2007 Springer-Verlag Berlin Heidelberg

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Lopez, L.D., Fuentes, O. (2007). Color-Based Road Sign Detection and Tracking. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_101

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  • DOI: https://doi.org/10.1007/978-3-540-74260-9_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74258-6

  • Online ISBN: 978-3-540-74260-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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