Robust traffic sign shape recognition using geometric matching

Robust traffic sign shape recognition using geometric matching

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A novel approach for recognising various traffic sign shapes in outdoor environments is presented. To reduce the influence of digital noise and extract the shape of each individual traffic sign, the external boundaries of traffic signs segmented based on colour information are simplified and decomposed through discrete curve evolution whose stop stage is determined by an arc similarity measure in tangent space. The recognition of a closed candidate shape is achieved through the direct matching with templates. An optimal enclosure is generated to minimise the geometric differences between the retrieved unclosed candidate shape and templates. The experimental results justify that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur.


    1. 1)
    2. 2)
      • C.Y. Fang , C.S. Fuh , P.S. Yen , S. Cherng , S.W. Chen . An automatic road sign recognition system based on a computational model of human recognition processing. Comput. Vis. Image Underst. , 237 - 268
    3. 3)
    4. 4)
      • Fleyeh, H.: `Shadow and highlight invariant colour segmentation algorithm for traffic signs', IEEE Conf. Cybernetics and Intelligent Systems, June 2006, Bangkok, Thailand, p. 1–7.
    5. 5)
      • Jiang, G.Y., Choi, T.Y.: `Robust detection of landmarks in color image based on fuzzy set theory', IEEE the 4th Int. Conf. Signal Processing, October 1998, Beijing, China, p. 968–971.
    6. 6)
      • Garcia-Garrido, M.A., Sotelo, M.A., Martin-Gorostiza, E.: `Fast traffic sign detection and recognition under changing lighting conditions', IEEE Intelligent Transportation Systems Conf., September 2006, Toronto, Canada, p. 811–816.
    7. 7)
      • Gavrila, D.M.: `Traffic sign recognition revisited', Proc. 21st DAGM Symp. fur Mustererkennung, 1999, Bonn, Germany, p. 86–93.
    8. 8)
    9. 9)
      • Loy, G., Barnes, N.: `Fast shape-based road sign detection for a driver assistance system', Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, September 2004, Sendai, Japan, p. 70–75.
    10. 10)
      • Gil-Jimenez, P., Lafuente-Arroyo, S., Gomez-Moreno, H., Lopez-Ferreras, F., Maldonado-Bascon, S.: `Traffic sign shape classification evaluation II: FFT applied to the signature of blobs', Proc. IEEE Intelligent Vehicles Symp., June 2005, Las Vegas, USA, p. 607–612.
    11. 11)
    12. 12)
      • Finlayson, G., Schaefer, G.: `Hue that is invariant to brightness and gamma', British Machine Vision Conf., September 2001, Manchester, UK, p. 303–312.
    13. 13)
    14. 14)
      • Asakura, T., Aoyagi, Y., Hirose, O.K.: `Real-time recognition of road traffic sign in moving scene image using new image filter', Proc. 39th SICE Annual Conf., July 2000, Lizuka, Japan, p. 13–18.
    15. 15)
    16. 16)
    17. 17)

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