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.
CITATION STYLE
Di Mauro, D., Battiato, S., Patanè, G., Leotta, M., Maio, D., & Farinella, G. M. (2016). Learning approaches for parking lots classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10016 LNCS, pp. 410–418). Springer Verlag. https://doi.org/10.1007/978-3-319-48680-2_36
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