Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Carrara F., Falchi F., Gennaro C., Meghini C., Vairo C. Deep learning for decentralized parking lot occupancy detection. In: Expert Systems with Applications, vol. 72 (April 2017) pp. 327 - 334. [Online First 29 Octover 2016] Elsevier, 2017.
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.
URL: http://www.sciencedirect.com/science/article/pii/S095741741630598X
DOI: 10.1016/j.eswa.2016.10.055
Subject Machine learning
Deep learning
Convolutional neural networks
Parking space dataset
I.2.10 ARTIFICIAL INTELLIGENCE. Vision and Scene Understanding

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