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Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Authors:

Azzedine Boukerche,

Liang SongAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 7

Article No.: 189, Pages 1 - 38

https://doi.org/10.1145/3645101

Published: 09 April 2024 Publication History

Abstract

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly navigates through unsupervised, weakly-supervised, supervised and fully-unsupervised VAD methodologies, elucidating the distinctions and interconnections within these research trajectories. In addition, this survey facilitates prospective researchers by assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature. Furthermore, this survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration.

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 7

July 2024

1006 pages

EISSN:1557-7341

DOI:10.1145/3613612

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

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Publication History

Published: 09 April 2024

Online AM: 07 February 2024

Accepted: 31 January 2024

Revised: 18 November 2023

Received: 10 February 2023

Published in CSUR Volume 56, Issue 7

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China Mobile Research Fund of the Chinese Ministry of Education
National Natural Science Foundation of China
Specific Research Fund of the Innovation Platform for Academicians of Hainan Province
Shanghai Key Research Laboratory of NSAI
Joint Laboratory on Networked AI Edge Computing Fudan University-Changan
China Scholarship Council

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Yang QWang CLiu PJiang ZLi J(2025)Video Anomaly Detection via self-supervised and spatio-temporal proxy tasks learningPattern Recognition10.1016/j.patcog.2024.111021158(111021)Online publication date: Feb-2025
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Cheng HLuo JZhang XLiu HWu F(2025)Towards training noise-robust anomaly detection via collaborative adversarial flowsMeasurement10.1016/j.measurement.2024.115865242(115865)Online publication date: Jan-2025
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Zhou YYang PQu YXu XSun ZCichocki A(2025)AnoOnly: Semi-supervised anomaly detection with the only loss on anomaliesExpert Systems with Applications10.1016/j.eswa.2024.125597262(125597)Online publication date: Mar-2025
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Zhao MZeng XLiu YLiu JPang C(2025)Rethinking prediction-based video anomaly detection from local–global normality perspectiveExpert Systems with Applications10.1016/j.eswa.2024.125581262(125581)Online publication date: Mar-2025
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Tao CWang CLin SCai SLi DQian J(2024)Feature Reconstruction With Disruption for Unsupervised Video Anomaly DetectionIEEE Transactions on Multimedia10.1109/TMM.2024.340571626(10160-10173)Online publication date: 2024
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Ji ZLv WHu JJin YQiu ZHuang J(2024)Dual-Stream Anomaly Detection Network for Real-World Traffic ScenariosIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.347627625:12(20779-20792)Online publication date: Dec-2024
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Pu YWu XYang LWang S(2024)Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly DetectionIEEE Transactions on Image Processing10.1109/TIP.2024.345193533(4923-4936)Online publication date: 1-Jan-2024
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Fang JQiao JXue JLi Z(2024)Vision-Based Traffic Accident Detection and Anticipation: A SurveyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.330765534:4(1983-1999)Online publication date: Apr-2024
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