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Improving video surveillance systems in banks using deep learning techniques

In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for prev...

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Autores principales: Zahrawi, Mohammad, Shaalan, Khaled
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188611/
https://www.ncbi.nlm.nih.gov/pubmed/37193787
http://dx.doi.org/10.1038/s41598-023-35190-9
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author Zahrawi, Mohammad
Shaalan, Khaled
author_facet Zahrawi, Mohammad
Shaalan, Khaled
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description In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based on real-time object detection systems for weapons auto-detection in video surveillance systems. We propose an early weapon detection framework using state-of-the-art, real-time object detection systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely reducing the number of false alarms in order to employ the model in real-life applications. The model is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth. The model can be employed as a precautionary system to prevent robberies by implying the model in outdoor surveillance cameras.
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spelling pubmed-101886112023-05-18 Improving video surveillance systems in banks using deep learning techniques Zahrawi, Mohammad Shaalan, Khaled Sci Rep Article In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based on real-time object detection systems for weapons auto-detection in video surveillance systems. We propose an early weapon detection framework using state-of-the-art, real-time object detection systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely reducing the number of false alarms in order to employ the model in real-life applications. The model is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth. The model can be employed as a precautionary system to prevent robberies by implying the model in outdoor surveillance cameras. Nature Publishing Group UK 2023-05-16 /pmc/articles/PMC10188611/ /pubmed/37193787 http://dx.doi.org/10.1038/s41598-023-35190-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Zahrawi, Mohammad
Shaalan, Khaled
Improving video surveillance systems in banks using deep learning techniques
title Improving video surveillance systems in banks using deep learning techniques
title_full Improving video surveillance systems in banks using deep learning techniques
title_fullStr Improving video surveillance systems in banks using deep learning techniques
title_full_unstemmed Improving video surveillance systems in banks using deep learning techniques
title_short Improving video surveillance systems in banks using deep learning techniques
title_sort improving video surveillance systems in banks using deep learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10188611/
https://www.ncbi.nlm.nih.gov/pubmed/37193787
http://dx.doi.org/10.1038/s41598-023-35190-9
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