Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Open accessAbstract
Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.
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Several transformer models lack mechanisms for incremental learning, explainability, or domain adaptation, which are critical requirements for long-term monitoring and dynamic urban environments.
“Additionally, several models lack mechanisms for incremental learning, explainability, or domain adaptation critical requirements for long-term monitoring and dynamic urban environments [5].”
From A Comprehensive Survey of Transformer-Based Models for Video Anomaly Detection in Surveillance Systems (2025) · cites this paper as [5]
Section 6.3 (page unavailable) identifies model interpretability and transfer learning across datasets with different distributions as future research directions, broadly supporting the explainability and adaptation concerns. It does not say that several transformer models lack these mechanisms, discuss incremental learning as a gap, or tie the requirements to long-term monitoring and dynamic urban environments. (LLM, conf=0.83)
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- Authors
- Duong, H.-T., Le, V.-T., & Hoang, V. T.
- Affiliations
- Ho Chi Minh City Open University (VN)
- Corresponding author
- Hoang, Vinh Truong
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-05-24
- Volume
- 23
- Issue
- 11
- Pages
- 5024
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10255829
- OpenAlex
- W4378228743
- Cited by
- 178
- References
- 132
- Citations by year
- 2026: 60 · 2025: 72 · 2024: 39 · 2023: 7
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ho Chi Minh City Open University, Vietnam
- Institute of Automation, Chinese Academy of Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- video anomaly detection, video surveillance, deep learning, generative models, human behavior analysis, video preprocessing, feature engineering, benchmark dataset, public security, video streaming
- MeSH terms
- Deep Learning, Engineering, Humans, Intelligence, Databases, Factual, Benchmarking
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