Learning Approaches for Parking Lots Classification

  • Daniele Di MauroEmail author
  • 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)


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.


Supervised learning Semi-supervised learning Convolutional Neural Networks 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Daniele Di Mauro
    • 1
    Email author
  • 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

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