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.
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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)
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)
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)
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)
Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition using tree classifiers. IEEE Trans. Intell. Transp. Syst. 13(4), 1507–1514 (2012)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
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)
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)
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)
Wang, M., Liu, X., Wu, X.: Visual classification by \(\ell \)1-hypergraph modeling. IEEE Trans. Knowl. Data Eng. 27(9), 2564–2574 (2015)
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)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
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)
Lee, C.Y., Xie, S., Gallagher, P., et al.: Deeply supervised nets. In: Proceedings of AISTATS (2015)
Girshick, R.: Fast RCNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)
Tang, S., Huang, L.L.: Traffic sign recognition using complementary features. In: IEEE Asian Conference on Pattern Recognition(ACPR), pp. 210–214 (2013)
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)
Liu, H., Liu, Y., Sun, F.: Traffic sign recognition using group sparse coding. Inf. Sci. 266, 75–89 (2014)
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)
Ciresan, D., Meier, U., Masci, J., et al.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)
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)
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)
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)
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)
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).
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Xie, K., Ge, S., Ye, Q., Luo, Z. (2016). Traffic Sign Recognition Based on Attribute-Refinement Cascaded Convolutional Neural Networks. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_20
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DOI: https://doi.org/10.1007/978-3-319-48890-5_20
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