2016
Conference article
Open Access
Car parking occupancy detection using smart camera networks and Deep Learning
Amato G., Carrara F., Falchi F., Gennaro C., Vairo C.
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.Source: IEEE Symposium on Computers and Communication, pp. 1212–1217, Messina, Italy, 27-30 June 2016
DOI: 10.1109/iscc.2016.7543901
Metrics:
Amato G., Carrara F., Falchi F., Gennaro C., Vairo C.
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.Source: IEEE Symposium on Computers and Communication, pp. 1212–1217, Messina, Italy, 27-30 June 2016
DOI: 10.1109/iscc.2016.7543901
Metrics:
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ISTI Repository 
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| ieeexplore.ieee.org | CNR ExploRA
2016
Report
Open Access
Picture it in your mind: generating high level visual representations from textual descriptions
Carrara F., Esuli A., Fagni T., Falchi F., Moreo Fernandez A.
In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the advantage that any update to the translation model does not require to reprocess the, typically huge, image collection on which the search is performed. We propose Text2Vis, a neural network that generates a visual representation, in the visual feature space of the fc6-fc7 layers of ImageNet, from a short descriptive text. Text2Vis optimizes two loss functions, using a stochastic loss-selection method. A visual-focused loss is aimed at learning the actual text-to-visual feature mapping, while a text-focused loss is aimed at modeling the higher-level semantic concepts expressed in language and countering the overfit on non-relevant visual components of the visual loss. We report preliminary results on the MS-COCO dataset.Source: ISTI Technical reports, 2016
Carrara F., Esuli A., Fagni T., Falchi F., Moreo Fernandez A.
In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the advantage that any update to the translation model does not require to reprocess the, typically huge, image collection on which the search is performed. We propose Text2Vis, a neural network that generates a visual representation, in the visual feature space of the fc6-fc7 layers of ImageNet, from a short descriptive text. Text2Vis optimizes two loss functions, using a stochastic loss-selection method. A visual-focused loss is aimed at learning the actual text-to-visual feature mapping, while a text-focused loss is aimed at modeling the higher-level semantic concepts expressed in language and countering the overfit on non-relevant visual components of the visual loss. We report preliminary results on the MS-COCO dataset.Source: ISTI Technical reports, 2016
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2016
Dataset
Unknown
CNRPark
Amato G., Carrara F., Falchi F., Gennaro C., Vairo C.
CNRPark+EXT is a dataset for visual occupancy detection of parking lots of roughly 150,000 labeled images (patches) of vacant and occupied parking spaces, built on a parking lot of 164 parking spaces. CNRPark+EXT extends CNRPark, a preliminary dataset composed by 12,000 images collected in different days of July 2015 from 2 cameras. The additional subset, called CNR-EXT, is composed by images collected from November 2015 to February 2016 under various weather conditions by 9 cameras with different perspectives and angles of view. CNR-EXT captures different situations of light conditions, and it includes partial occlusion patterns due to obstacles (trees, lampposts, other cars) and partial or global shadowed cars. The video shows the visual occupancy detection system based deployed at the CNR Research Area in Pisa, Italy. Predictions are made by the cameras, which run an efficient convolutional neural network classifier trained with CNRPark+EXT. CNRPark+EXT is composed by two subsets collected during our research. In the following, the details of both subsets are reported, together with a preview of the data collected.
Amato G., Carrara F., Falchi F., Gennaro C., Vairo C.
CNRPark+EXT is a dataset for visual occupancy detection of parking lots of roughly 150,000 labeled images (patches) of vacant and occupied parking spaces, built on a parking lot of 164 parking spaces. CNRPark+EXT extends CNRPark, a preliminary dataset composed by 12,000 images collected in different days of July 2015 from 2 cameras. The additional subset, called CNR-EXT, is composed by images collected from November 2015 to February 2016 under various weather conditions by 9 cameras with different perspectives and angles of view. CNR-EXT captures different situations of light conditions, and it includes partial occlusion patterns due to obstacles (trees, lampposts, other cars) and partial or global shadowed cars. The video shows the visual occupancy detection system based deployed at the CNR Research Area in Pisa, Italy. Predictions are made by the cameras, which run an efficient convolutional neural network classifier trained with CNRPark+EXT. CNRPark+EXT is composed by two subsets collected during our research. In the following, the details of both subsets are reported, together with a preview of the data collected.
See at: cnrpark.it | CNR ExploRA
2016
Report
Open Access
ProgettISTI 2016
Banterle F., Barsocchi P., Candela L., Carlini E., Carrara F., Cassarà P., Ciancia V., Cintia P., Dellepiane M., Esuli A., Gabrielli L., Germanese D., Girardi M., Girolami M., Kavalionak H., Lonetti F., Lulli A., Moreo Fernandez A., Moroni D., Nardini F. M., Monteiro De Lira V. C., Palumbo F., Pappalardo L., Pascali M. A., Reggianini M., Righi M., Rinzivillo S., Russo D., Siotto E., Villa A.
ProgettISTI research project grant is an award for members of the Institute of Information Science and Technologies (ISTI) to provide support for innovative, original and multidisciplinary projects of high quality and potential. The choice of theme and the design of the research are entirely up to the applicants yet (i) the theme must fall under the ISTI research topics, (ii) the proposers of each project must be of diverse laboratories of the Institute and must contribute different expertise to the project idea, and (iii) project proposals should have a duration of 12 months. This report documents the procedure, the proposals and the results of the 2016 edition of the award. In this edition, ten project proposals have been submitted and three of them have been awarded.Source: ISTI Technical reports, 2016
Banterle F., Barsocchi P., Candela L., Carlini E., Carrara F., Cassarà P., Ciancia V., Cintia P., Dellepiane M., Esuli A., Gabrielli L., Germanese D., Girardi M., Girolami M., Kavalionak H., Lonetti F., Lulli A., Moreo Fernandez A., Moroni D., Nardini F. M., Monteiro De Lira V. C., Palumbo F., Pappalardo L., Pascali M. A., Reggianini M., Righi M., Rinzivillo S., Russo D., Siotto E., Villa A.
ProgettISTI research project grant is an award for members of the Institute of Information Science and Technologies (ISTI) to provide support for innovative, original and multidisciplinary projects of high quality and potential. The choice of theme and the design of the research are entirely up to the applicants yet (i) the theme must fall under the ISTI research topics, (ii) the proposers of each project must be of diverse laboratories of the Institute and must contribute different expertise to the project idea, and (iii) project proposals should have a duration of 12 months. This report documents the procedure, the proposals and the results of the 2016 edition of the award. In this edition, ten project proposals have been submitted and three of them have been awarded.Source: ISTI Technical reports, 2016
See at:
ISTI Repository | CNR ExploRA