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.
Inventions 2018, 3(4), 69; https://doi.org/10.3390/inventions3040069
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)
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
▼
Show Figures
Figure 1
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
MDPI and ACS Style
Turchini, F.; Seidenari, L.; Uricchio, T.; Del Bimbo, A. Deep Learning Based Surveillance System for Open Critical Areas. Inventions 2018, 3, 69. https://doi.org/10.3390/inventions3040069
AMA Style
Turchini F, Seidenari L, Uricchio T, Del Bimbo A. Deep Learning Based Surveillance System for Open Critical Areas. Inventions. 2018; 3(4):69. https://doi.org/10.3390/inventions3040069
Chicago/Turabian StyleTurchini, Francesco, Lorenzo Seidenari, Tiberio Uricchio, and Alberto Del Bimbo. 2018. "Deep Learning Based Surveillance System for Open Critical Areas" Inventions 3, no. 4: 69. https://doi.org/10.3390/inventions3040069
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
