2017
Journal article  Open Access

Deep learning for decentralized parking lot occupancy detection

Amato G., Carrara F., Falchi F., Gennaro C., Meghini C., Vairo C.

Deep learning  Parking space dataset  Machine learning  Classification  Convolutional neural networks 

A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.

Source: Expert systems with applications 72 (2017): 327–334. doi:10.1016/j.eswa.2016.10.055

Publisher: Pergamon,, Oxford , Regno Unito



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BibTeX entry
@article{oai:it.cnr:prodotti:366883,
	title = {Deep learning for decentralized parking lot occupancy detection},
	author = {Amato G. and Carrara F. and Falchi F. and Gennaro C. and Meghini C. and Vairo C.},
	publisher = {Pergamon,, Oxford , Regno Unito},
	doi = {10.1016/j.eswa.2016.10.055},
	journal = {Expert systems with applications},
	volume = {72},
	pages = {327–334},
	year = {2017}
}