CNRPark+EXT - Papers
[1] Deep learning for decentralized parking lot occupancy detection
G Amato, F Carrara, F Falchi, C Gennaro, C Meghini, C Vairo
https://www.sciencedirect.com/science/article/pii/S095741741630598X
Expert Systems with Applications 72, 327-334
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.
[2] Car parking occupancy detection using smart camera networks and deep learning
https://ieeexplore.ieee.org/abstract/document/7543901/
G Amato, F Carrara, F Falchi, C Gennaro, C Vairo
IEEE Symposium on Computers and Communication (ISCC) 2016, 1212-1217
This paper presents an approach for real-time car parking occupancy detection that uses a Convolutional Neural Network (CNN) classifier running on-board of a smart camera with limited resources. Experiments show that our technique is very effective and robust to light condition changes, presence of shadows, and partial occlusions. The detection is reliable, even when tests are performed using images captured from a viewpoint different than the viewpoint used for training. In addition, it also demonstrates its robustness when training and tests are executed on different parking lots. We have tested and compared our solution against state of the art techniques, using a reference benchmark for parking occupancy detection. We have also produced and made publicly available an additional dataset that contains images of the parking lot taken from different viewpoints and in different days with different light conditions. The dataset captures occlusion and shadows that might disturb the classification of the parking spaces status.
@article{amato2017deep,
title={Deep learning for decentralized parking lot occupancy detection},
author={Amato, Giuseppe and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Meghini, Carlo and Vairo, Claudio},
journal={Expert Systems with Applications},
volume={72},
pages={327--334},
year={2017},
publisher={Pergamon}
}
@inproceedings{amato2016car,
title={Car parking occupancy detection using smart camera networks and deep learning},
author={Amato, Giuseppe and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Vairo, Claudio},
booktitle={Computers and Communication (ISCC), 2016 IEEE Symposium on},
pages={1212--1217},
year={2016},
organization={IEEE}
}References
http://www.cnrpark.it/
https://www.sciencedirect.com/science/article/pii/S095741741630598X
http://ieeexplore.ieee.org/abstract/document/7543901/