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Computation
Volume 10
Issue 3
10.3390/computation10030035
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Article

A Video Analytics System for Person Detection Combined with Edge Computing

by
Evangelos Maltezos
*,
Panagiotis Lioupis
,
Aris Dadoukis
,
Lazaros Karagiannidis
,
Eleftherios Ouzounoglou
,
Maria Krommyda
and
Angelos Amditis
Institute of Communication and Computer Systems (ICCS), 15773 Zografou, Greece
*
Author to whom correspondence should be addressed.
Computation 2022, 10(3), 35; https://doi.org/10.3390/computation10030035
Submission received: 2 February 2022 / Revised: 21 February 2022 / Accepted: 24 February 2022 / Published: 25 February 2022
(This article belongs to the Special Issue Computation and Analysis of Remote Sensing Imagery and Image Motion)
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Abstract

Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness.
Keywords: person detection; object detection; object tracking; smart cities; situational awareness; edge computing; computer vision; YOLOv5; terrestrial; vehicle person detection; object detection; object tracking; smart cities; situational awareness; edge computing; computer vision; YOLOv5; terrestrial; vehicle

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MDPI and ACS Style

Maltezos, E.; Lioupis, P.; Dadoukis, A.; Karagiannidis, L.; Ouzounoglou, E.; Krommyda, M.; Amditis, A. A Video Analytics System for Person Detection Combined with Edge Computing. Computation 2022, 10, 35. https://doi.org/10.3390/computation10030035

AMA Style

Maltezos E, Lioupis P, Dadoukis A, Karagiannidis L, Ouzounoglou E, Krommyda M, Amditis A. A Video Analytics System for Person Detection Combined with Edge Computing. Computation. 2022; 10(3):35. https://doi.org/10.3390/computation10030035

Chicago/Turabian Style

Maltezos, Evangelos, Panagiotis Lioupis, Aris Dadoukis, Lazaros Karagiannidis, Eleftherios Ouzounoglou, Maria Krommyda, and Angelos Amditis. 2022. "A Video Analytics System for Person Detection Combined with Edge Computing" Computation 10, no. 3: 35. https://doi.org/10.3390/computation10030035

APA Style

Maltezos, E., Lioupis, P., Dadoukis, A., Karagiannidis, L., Ouzounoglou, E., Krommyda, M., & Amditis, A. (2022). A Video Analytics System for Person Detection Combined with Edge Computing. Computation, 10(3), 35. https://doi.org/10.3390/computation10030035

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