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.
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Di Mauro, D., Battiato, S., Patanè, G., Leotta, M., Maio, D., Farinella, G.M. (2016). Learning Approaches for Parking Lots Classification. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_36
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DOI: https://doi.org/10.1007/978-3-319-48680-2_36
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