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Unsupervised Data Augmentation for Improving Traffic Sign Recognition

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PRICAI 2019: Trends in Artificial Intelligence (PRICAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11672))

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Abstract

Traffic sign recognition is a key function in driver assistant systems and autonomous vehicles. Several benchmark datasets had been proposed to test the performance of various recognition models. However, two related problems remained unsolved. First, whether the data samples are enough to evaluate the performance of the proposed recognition models? Second, whether data augmentation could be introduced to build better benchmark datasets? To solve these two problems, we show in this paper that some famous benchmark datasets can be further improved via appropriate data augmentation. Specially, we propose a feature-space data augmentation algorithm that first determines an appropriate feature space for the available data, then generates potentially useful new samples in the feature space and finally maps these new samples into original spaces to get new data samples. Numerical tests show that this algorithm helps to increase the accuracies of recognition models.

S. Cao and W. Zheng—Contribute equally to this study.

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References

  1. Abukhait, J., Zyout, I., Mansour, A.M.: Speed sign recognition using shape-based features. Int. J. Comput. Appl. 84, 31–37 (2013)

    Google Scholar 

  2. Anderson, D.T., Zare, A., Price, S.: Comparing fuzzy, probabilistic, and possibilistic partitions using the earth mover ’s distance. IEEE Trans. Fuzzy Syst. 21(4), 766–775 (2013)

    Article  Google Scholar 

  3. Baro, X., Escalera, S., Vitria, J., Pujol, O., Radeva, P.: Traffic sign recognition using evolutionary adaboost detection and forest-ecoc classification. IEEE Trans. Intell. Transp. Syst. 10(1), 113–126 (2009)

    Article  Google Scholar 

  4. Bos, R., de Waele, S., Broersen, P.M.T.: Autoregressive spectral estimation by application of the burg algorithm to irregularly sampled data. IEEE Trans. Instrum. Meas. 51(6), 1289–1294 (2002)

    Article  Google Scholar 

  5. Chen, T., Lu, S.: Accurate and efficient traffic sign detection using discriminative adaboost and support vector regression. IEEE Trans. Veh. Technol. 65(6), 4006–4015 (2016)

    Article  Google Scholar 

  6. Courty, N., Flamary, R., Tuia, D.: Domain adaptation with regularized optimal transport. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 274–289. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44848-9_18

    Chapter  Google Scholar 

  7. DeVries, T., Taylor, G.W.: Dataset Augmentation in Feature Space. arXiv e-prints, February 2017

    Google Scholar 

  8. Flamary, R., Courty, N.: Pot python optimal transport library (2017)

    Google Scholar 

  9. Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv e-prints, June 2014

    Google Scholar 

  10. Greenhalgh, J., Mirmehdi, M.: Real-time detection and recognition of road traffic signs. IEEE Trans. Intell. Transp. Syst. 13(4), 1498–1506 (2012)

    Article  Google Scholar 

  11. Hara, S., Katsuki, T., Yanagisawa, H., Ono, T., Okamoto, R., Takeuchi, S.: Consistent and efficient nonparametric different-feature selection. In: Artificial Intelligence and Statistics, pp. 130–138 (2017)

    Google Scholar 

  12. Hillebrand, M., Kreßel, U., Wöhler, C., Kummert, F.: Traffic sign classifier adaption by semi-supervised co-training. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS (LNAI), vol. 7477, pp. 193–200. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33212-8_18

    Chapter  Google Scholar 

  13. Houben, S., Stallkamp, J., Salmen, J., Schlipsing, M., Igel, C.: Detection of traffic signs in real-world images: the german traffic sign detection benchmark. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, August 2013

    Google Scholar 

  14. Huang, W., Wen, D., Geng, J., Zheng, N.: Task-specific performance evaluation of UGVs: case studies at the IVFC. IEEE Trans. Intell. Transp. Syst. 15(5), 1969–1979 (2014)

    Article  Google Scholar 

  15. Huang, Z., Yu, Y., Gu, J., Liu, H.: An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans. Cybern. 47(4), 920–933 (2017)

    Article  Google Scholar 

  16. Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv e-prints, January 2014

    Google Scholar 

  17. Kalayeh, M.M., Idrees, H., Shah, M.: NMF-KNN: image annotation using weighted multi-view non-negative matrix factorization. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 184–191, June 2014

    Google Scholar 

  18. Kingma, D.P., Dhariwal, P.: Glow: Generative Flow with Invertible 1x1 Convolutions. arXiv e-prints, July 2018

    Google Scholar 

  19. Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. arXiv e-prints, December 2013

    Google Scholar 

  20. Lei, N., Luo, Z., Yau, S.-T., Gu, D.X.: Geometric Understanding of Deep Learning. arXiv e-prints, May 2018

    Google Scholar 

  21. Lei, N., Su, K., Cui, L., Yau, S.-T., Gu, D.X.: A Geometric View of Optimal Transportation and Generative Model. arXiv e-prints, October 2017

    Google Scholar 

  22. Li, L., Huang, W., Liu, Y., Zheng, N., Wang, F.: Intelligence testing for autonomous vehicles: a new approach. IEEE Trans. Intell. Veh. 1(2), 158–166 (2016)

    Article  Google Scholar 

  23. Li, L., Wang, F.Y.: Advanced motion control and sensing for intelligent vehicles (2007)

    Google Scholar 

  24. Li, Y., Møgelmose, A., Trivedi, M.M.: Pushing the “speed limit”: high-accuracy us traffic sign recognition with convolutional neural networks. IEEE Trans. Intell. Veh. 1(2), 167–176 (2016)

    Article  Google Scholar 

  25. 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)

    Article  Google Scholar 

  26. Mogelmose, A., Trivedi, M.M., Moeslund, T.B.: Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13(4), 1484–1497 (2012)

    Article  Google Scholar 

  27. Ning, L., Georgiou, T.T., Tannenbaum, A.: Matrix-valued monge-kantorovich optimal mass transport. In: 52nd IEEE Conference on Decision and Control, pp. 3906–3911, December 2013

    Google Scholar 

  28. Perrot, M., Courty, N., Flamary, R., Habrard, A.: Mapping estimation for discrete optimal transport. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29, pp. 4197–4205. Curran Associates Inc., New York (2016)

    Google Scholar 

  29. Sermanet, P., LeCun, Y.: Traffic sign recognition with multi-scale convolutional networks. In: The 2011 International Joint Conference on Neural Networks, pp. 2809–2813, July 2011

    Google Scholar 

  30. Shi, M., Wu, H., Fleyeh, H.: Support vector machines for traffic signs recognition. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 3820–3827, June 2008

    Google Scholar 

  31. Watrigant, R., Bougeret, M., Giroudeau, R.: Approximating the sparsest k-subgraph in chordal graphs. Theory Comput. Syst. 58(1), 111–132 (2016)

    Article  MathSciNet  Google Scholar 

  32. Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding data augmentation for classification: when to warp? In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–6, November 2016

    Google Scholar 

  33. Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17(7), 2022–2031 (2016)

    Article  Google Scholar 

  34. Yao, C., Wu, F., Chen, H., Hao, X., Shen, Y.: Traffic sign recognition using hog-SVM and grid search. In: 2014 12th International Conference on Signal Processing (ICSP), pp. 962–965, October 2014

    Google Scholar 

  35. Yao, C., Cai, D., Jiajun, B., Chen, G.: Pre-training the deep generative models with adaptive hyperparameter optimization. Neurocomputing 247, 144–155 (2017)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Sisi Cao & Wenbo Zheng

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Shaocong Mo

Authors
  1. Sisi Cao
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  2. Wenbo Zheng
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  3. Shaocong Mo
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Corresponding authors

Correspondence to Wenbo Zheng or Shaocong Mo .

Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, Sydney, NSW, Australia

    Abhaya C. Nayak

  2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

A A Feature-Select Solution Based Wasserstein Distance

A A Feature-Select Solution Based Wasserstein Distance

The Feature-Select-Solution function is shown in Algorithm 2.

figure b

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Cao, S., Zheng, W., Mo, S. (2019). Unsupervised Data Augmentation for Improving Traffic Sign Recognition. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_25

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  • DOI: https://doi.org/10.1007/978-3-030-29894-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29893-7

  • Online ISBN: 978-3-030-29894-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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