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Radial Basis Function Network for Traffic Scene Classification in Single Image Mode

  • Qiao Huang
  • Jianming Hu
  • Jingyan Song
  • Tianliang Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

In this paper, a radial basis function (RBF) network based method inspired by mature algorithms for face recognition is applied to classify traffic scenes in single image mode. Not to follow traditional ways of estimating traffic states through image segmentation and vehicle tracking, this method avoids complicated problems in digital image processing (DIP) and can operate on just one image, while the old ones rely on consecutive images. The proposed method adopts discrete cosine transform (DCT) for feature selection, then a supervised clustering algorithm is fulfilled to help design hidden layer of RBF network for which Gaussian function is chosen, finally linear least square (LLS) is used to solve the weights training problem. Experiments show that this method is valid and effective under the new application background.

Keywords

Radial Base Function Face Recognition Discrete Cosine Transform Traffic State Radial Base Function Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Benjamin, M., Osama, M., Nikolaos, P.P.: Tracking All Traffic. IEEE Robotics and Automation, 29–36 (2005)Google Scholar
  2. 2.
    Hsu, W.L., Liao, H.Y.M., Jeng, B.S., Fan, K.C.: Real-time traffic parameter extraction using entropy. In: IEE Proceedings of Vision, Image and Signal Process, pp. 194–202 (2004)Google Scholar
  3. 3.
    Fatih, P., Li, X.K.: Traffic Congestion Estimation Using HMM Models Without Vehicle Tracking. IEEE Intelligent Vehicles Symposium, 188–193 (2004)Google Scholar
  4. 4.
    Yang, F., Michel, P.: Implementation of an RBF Neural Network on Embedded Systems: Real-Time Face Tracking and Identity Verification. IEEE Transactions on Neural Networks 14(5), 1162–1175 (2003)CrossRefGoogle Scholar
  5. 5.
    Pan, Z.J., Alistair, G.R., Hamid, B.: Image Redundancy Reduction for Neural Network Classification using Discrete Cosine Transforms. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp. 149–154 (2000)Google Scholar
  6. 6.
    Meng, J.E., Chen, W.L., Wu, S.Q.: High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks. IEEE Transactions on Neural Networks 16(3), 679–690 (2005)CrossRefGoogle Scholar
  7. 7.
    Smith, R.M., Johansen, T.A.: Local Learning in Local Model Networks. In: IEEE International Conference on Artificial Neural Networks, pp. 40–46 (1995)Google Scholar
  8. 8.
    Tarassenko, L., Roberts, S.: Supervised and Unsupervised Learning in Radial Basis Function Classifiers. In: IEE Proceedings of Vision, Image and Signal Processing, pp. 210–216 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Qiao Huang
    • 1
  • Jianming Hu
    • 1
  • Jingyan Song
    • 1
  • Tianliang Gao
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingChina

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