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Inventions 2018, 3(4), 69;

Deep Learning Based Surveillance System for Open Critical Areas

Department of Information Engineeering, University of Florence, Via di Santa Marta, 3, 50139 Firenze, Italy
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Received: 3 August 2018 / Revised: 3 October 2018 / Accepted: 4 October 2018 / Published: 11 October 2018
(This article belongs to the Special Issue Innovation in Machine Intelligence for Critical Infrastructures)
PDF [1088 KB, uploaded 11 October 2018]


How to automatically monitor wide critical open areas is a challenge to be addressed. Recent computer vision algorithms can be exploited to avoid the deployment of a large amount of expensive sensors. In this work, we propose our object tracking system which, combined with our recently developed anomaly detection system. can provide intelligence and protection for critical areas. In this work. we report two case studies: an international pier and a city parking lot. We acquire sequences to evaluate the effectiveness of the approach in challenging conditions. We report quantitative results for object counting, detection, parking analysis, and anomaly detection. Moreover, we report state-of-the-art results for statistical anomaly detection on a public dataset. View Full-Text
Keywords: anomaly detection; surveillance systems; computer vision; object detection; object tracking anomaly detection; surveillance systems; computer vision; object detection; object tracking

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Turchini, F.; Seidenari, L.; Uricchio, T.; Del Bimbo, A. Deep Learning Based Surveillance System for Open Critical Areas. Inventions 2018, 3, 69.

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