Advertisement

Learning Approaches for Parking Lots Classification

  • Daniele Di Mauro
  • Sebastiano Battiato
  • Giuseppe Patanè
  • Marco Leotta
  • Daniele Maio
  • Giovanni M. Farinella
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)

Abstract

The paper exploits the problem of empty vs. non-empty parking lots classification from images acquired by public cameras through the comparison between a classic supervised learning method and a semi-supervised learning one. Both approaches are based on convolutional neural networks paradigm. Experimental results point out that the supervised method outperforms the semi-supervised approach already when few samples are used for training.

Keywords

Supervised learning Semi-supervised learning Convolutional Neural Networks 

References

  1. 1.
    de Almeida, P.R., Oliveira, L.S., Britto, A.S., Silva, E.J., Koerich, A.L.: PKLot-a robust dataset for parking Lot classification. Expert Syst. Appl. 42(11), 4937–4949 (2015)CrossRefGoogle Scholar
  2. 2.
    Battiato, S., Farinella, G.M., Furnari, A., Puglisi, G., Snijders, A., Spiekstra, J.: An integrated system for vehicle tracking and classification. Expert Syst. Appl. 42(21), 7263–7275 (2015)CrossRefGoogle Scholar
  3. 3.
    Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., Scholl, H.J.: Understanding smart cities: an integrative framework. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp. 2289–2297. IEEE (2012)Google Scholar
  4. 4.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  6. 6.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  7. 7.
    Lee, D.H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3 (2013)Google Scholar
  8. 8.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  9. 9.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69905-7_27 CrossRefGoogle Scholar
  10. 10.
    Shapiro, J.M.: Smart cities: quality of life, productivity, and the growth effects of human capital. Rev. Econ. Stat. 88(2), 324–335 (2006)CrossRefGoogle Scholar
  11. 11.
    Wu, Q., Zhang, Y.: Parking Lots Space Detection. Machine Learning, Fall (2006)Google Scholar
  12. 12.
    Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Daniele Di Mauro
    • 1
  • Sebastiano Battiato
    • 1
  • Giuseppe Patanè
    • 2
  • Marco Leotta
    • 2
  • Daniele Maio
    • 2
  • Giovanni M. Farinella
    • 1
  1. 1.Image Processing LabUniversity of CataniaCataniaItaly
  2. 2.Parksmart s.r.l.CataniaItaly

Personalised recommendations