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Traffic Sign Recognition Based on Attribute-Refinement Cascaded Convolutional Neural Networks

  • Kaixuan Xie
  • Shiming GeEmail author
  • Qiting Ye
  • Zhao Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Traffic sign recognition is a critical module of intelligent transportation system. Observing that a subtle difference may cause misclassification when the actual class and the predictive class share the same attributes such as shape, color, function and so on, we propose a two-stage cascaded convolutional neural networks (CNNs) framework, called attribute-refinement cascaded CNNs, to train the traffic sign classifier by taking full advantage of attribute-supervisory signals. The first stage CNN is trained with class label as supervised signals, while the second stage CNN is trained on super classes separately according to auxiliary attributes of traffic signs for further refinement. Experiments show that the proposed hierarchical cascaded framework can extract the deep information of similar categories, improve discrimination of the model and increase classification accuracy of traffic signs.

Keywords

Traffic sign recognition Convolutional Neural Network Attribute supervision Deep learning 

Notes

Acknowledgments

This work is supported in part by the National Key Research and Development Plan (Grant No.2016YFC0801005) and the National Natural Science Foundation of China (Grant No.61402463).

References

  1. 1.
    Fleyeh, H., Dougherty, M.: Road and traffic sign detection and recognition. In: Proceedings of the 16th Mini-EURO Conference and 10th Meeting of EWGT, pp. 644–653 (2005)Google Scholar
  2. 2.
    Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323–332 (2012)CrossRefGoogle Scholar
  3. 3.
    Wang, M., Gao, Y., Lu, K., Rui, Y.: View-based discriminative probabilistic modeling for 3D object retrieval and recognition. IEEE Trans. Image Process. 22(4), 1395–1407 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zaklouta, F., Stanciulescu, B., Hamdoun, O.: Traffic sign classification using k-d trees and random forests. In: IEEE Proceedings of International Joint Conference on Neural Networks (IJCNN), pp. 2151–2155 (2011)Google Scholar
  5. 5.
    Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition using tree classifiers. IEEE Trans. Intell. Transp. Syst. 13(4), 1507–1514 (2012)CrossRefGoogle Scholar
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  7. 7.
    Maldonado-Bascon, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gomez-Moreno, H., et al.: Road sign detection and recognition based on support vector machines. IEEE Trans. Intell. Transp. Syst. 8(2), 264–278 (2007)CrossRefGoogle Scholar
  8. 8.
    Shi, M., Wu, H., Fleyeh, H.: Support vector machines for traffic signs recognition. In: IEEE Proceedings of International Joint Conference on Neural Networks (IJCNN), pp. 3820–3827 (2008)Google Scholar
  9. 9.
    Wang, M., Fu, W., Hao, S., et al.: Scalable semi-supervised learning by efficient anchor graph regularization. IEEE Trans. Knowl. Data Eng. 28(7), 1864–1877 (2016)CrossRefGoogle Scholar
  10. 10.
    Wang, M., Liu, X., Wu, X.: Visual classification by \(\ell \)1-hypergraph modeling. IEEE Trans. Knowl. Data Eng. 27(9), 2564–2574 (2015)CrossRefGoogle Scholar
  11. 11.
    Huang, Z., Yu, Y., Gu, J., et al.: An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans. Cybern. 99, 1–14 (2016)Google Scholar
  12. 12.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  13. 13.
    Sun, Y., Chen, Y., Wang, X., et al.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems (NIPS), pp. 1988–1996 (2014)Google Scholar
  14. 14.
    Lee, C.Y., Xie, S., Gallagher, P., et al.: Deeply supervised nets. In: Proceedings of AISTATS (2015)Google Scholar
  15. 15.
    Girshick, R.: Fast RCNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)Google Scholar
  16. 16.
    Tang, S., Huang, L.L.: Traffic sign recognition using complementary features. In: IEEE Asian Conference on Pattern Recognition(ACPR), pp. 210–214 (2013)Google Scholar
  17. 17.
    Lu, K., Ding, Z., Ge, S.: Sparse-representation-based graph embedding for traffic sign recognition. IEEE Trans. Intell. Transp. Syst. 13(4), 1515–1524 (2012)CrossRefGoogle Scholar
  18. 18.
    Liu, H., Liu, Y., Sun, F.: Traffic sign recognition using group sparse coding. Inf. Sci. 266, 75–89 (2014)CrossRefGoogle Scholar
  19. 19.
    Ciresan, D., Meier, U., Masci, J., et al.: A committee of neural networks for traffic sign classification. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1918–1921 (2011)Google Scholar
  20. 20.
    Ciresan, D., Meier, U., Masci, J., et al.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)CrossRefGoogle Scholar
  21. 21.
    Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 2809–2813 (2011)Google Scholar
  22. 22.
    Jin, J., Fu, K., Zhang, C.: Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Trans. Intell. Transp. Syst. 15(5), 1991–2000 (2014)CrossRefGoogle Scholar
  23. 23.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)Google Scholar
  24. 24.
    Xie, K., Ge, S., Yang, R., et al.: Negative-supervised cascaded deep learning for traffic sign classification. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds.) CCCV 2015, Part I, vol. 546, pp. 249–257. Springer, Heidelberg (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kaixuan Xie
    • 1
    • 2
  • Shiming Ge
    • 1
    Email author
  • Qiting Ye
    • 1
    • 2
  • Zhao Luo
    • 1
    • 2
  1. 1.Beijing Key Laboratory of IOT Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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