Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2026

Abstract

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present an extensive and comprehensive research review, covering the spectrum of five different categories, namely, semi-supervised, weakly supervised, fully supervised, unsupervised, and open-set supervised VAD, and we also delve into the latest VAD works based on pretrained large models and open-world learning, remedying the limitations of past reviews in terms of only focusing on semi-supervised VAD and small model-based methods. For the VAD task with different levels of supervision, we construct a well-organized taxonomy, profoundly discuss the characteristics of different types of methods, and show their performance comparisons. In addition, this review involves the public datasets, open-source codes, and evaluation metrics covering all the aforementioned VAD tasks. Finally, we provide several important research directions for the VAD community. Additional details of the survey are available on the project homepage: https://github.com/RocNg/DeepVAD

Keywords

Anomaly detection, deep learning, large models, video anomaly detection (VAD), video understanding

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

37

Issue

7

First Page

3010

Last Page

3030

ISSN

2162-237X

Identifier

10.1109/TNNLS.2025.3647892

Publisher

Institute of Electrical and Electronics Engineers

Embargo Period

7-17-2026

Additional URL

https://doi.org/10.1109/TNNLS.2025.3647892

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