A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection

Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They are deployed in places such as markets, hospitals, schools, banks, shopping malls, offices, and smart cities. VSSs generate a massive amount of surveillance data, and signi...
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Autor*in:	Nomica Choudhry [verfasserIn] Jemal Abawajy [verfasserIn] Shamsul Huda [verfasserIn] Imran Rao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Machine learning anomaly detection video surveillance systems supervised learning methods unsupervised learning methods semi-supervised learning methods

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 114680-114713
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:114680-114713

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3321800

Katalog-ID:	DOAJ101332351

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source	In IEEE Access 11(2023), Seite 114680-114713 volume:11 year:2023 pages:114680-114713
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author	Nomica Choudhry
spellingShingle	Nomica Choudhry misc TK1-9971 misc Machine learning misc anomaly detection misc video surveillance systems misc supervised learning methods misc unsupervised learning methods misc semi-supervised learning methods misc Electrical engineering. Electronics. Nuclear engineering A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection
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topic_title	TK1-9971 A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection Machine learning anomaly detection video surveillance systems supervised learning methods unsupervised learning methods semi-supervised learning methods
topic	misc TK1-9971 misc Machine learning misc anomaly detection misc video surveillance systems misc supervised learning methods misc unsupervised learning methods misc semi-supervised learning methods misc Electrical engineering. Electronics. Nuclear engineering
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title	A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection
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title_auth	A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection
abstract	Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They are deployed in places such as markets, hospitals, schools, banks, shopping malls, offices, and smart cities. VSSs generate a massive amount of surveillance data, and significant research has been published on the use of machine learning algorithms to handle surveillance data. In this paper, we present an extensive overview and a thorough analysis of cutting-edge learning methods used in VSSs. Existing surveys on learning approaches in video surveillance have some drawbacks, such as a lack of in-depth analysis of the learning algorithms, omission of certain methodologies, insufficient critical evaluation, and absence of recent learning algorithms. To fill these gaps, this survey provides a thorough examination of the most recent learning algorithms for anomaly detection. A critical assessment of the algorithms including their strengths, weaknesses, and applicability as well as tailored classifications of anomaly types for different domains are provided. Our study also offers insights into the future development of learning techniques in VSS, positioning itself as a valuable resource for both researchers and practitioners in the field. Finally, we share our thoughts on what we learned and how it can help with new developments in the future.
abstractGer	Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They are deployed in places such as markets, hospitals, schools, banks, shopping malls, offices, and smart cities. VSSs generate a massive amount of surveillance data, and significant research has been published on the use of machine learning algorithms to handle surveillance data. In this paper, we present an extensive overview and a thorough analysis of cutting-edge learning methods used in VSSs. Existing surveys on learning approaches in video surveillance have some drawbacks, such as a lack of in-depth analysis of the learning algorithms, omission of certain methodologies, insufficient critical evaluation, and absence of recent learning algorithms. To fill these gaps, this survey provides a thorough examination of the most recent learning algorithms for anomaly detection. A critical assessment of the algorithms including their strengths, weaknesses, and applicability as well as tailored classifications of anomaly types for different domains are provided. Our study also offers insights into the future development of learning techniques in VSS, positioning itself as a valuable resource for both researchers and practitioners in the field. Finally, we share our thoughts on what we learned and how it can help with new developments in the future.
abstract_unstemmed	Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They are deployed in places such as markets, hospitals, schools, banks, shopping malls, offices, and smart cities. VSSs generate a massive amount of surveillance data, and significant research has been published on the use of machine learning algorithms to handle surveillance data. In this paper, we present an extensive overview and a thorough analysis of cutting-edge learning methods used in VSSs. Existing surveys on learning approaches in video surveillance have some drawbacks, such as a lack of in-depth analysis of the learning algorithms, omission of certain methodologies, insufficient critical evaluation, and absence of recent learning algorithms. To fill these gaps, this survey provides a thorough examination of the most recent learning algorithms for anomaly detection. A critical assessment of the algorithms including their strengths, weaknesses, and applicability as well as tailored classifications of anomaly types for different domains are provided. Our study also offers insights into the future development of learning techniques in VSS, positioning itself as a valuable resource for both researchers and practitioners in the field. Finally, we share our thoughts on what we learned and how it can help with new developments in the future.
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title_short	A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection
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