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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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 |
author_sort | Zahrawi, Mohammad |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-10188611 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zahrawimohammad improvingvideosurveillancesystemsinbanksusingdeeplearningtechniques AT shaalankhaled improvingvideosurveillancesystemsinbanksusingdeeplearningtechniques |