# An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos

arXiv: https://arxiv.org/abs/1801.03149 · HTML: https://mlsanity.com/paper/1801.03149/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2018.

## Built on by (most cited first, 30 of 45)

- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [Self-Supervised Masking for Unsupervised Anomaly Detection and Localization](https://mlsanity.com/paper/2205.06568/) (2022)
- [Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series](https://mlsanity.com/paper/2202.07857/) (2022)
- [Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals](https://mlsanity.com/paper/2107.12626/) (2021)
- [Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook](https://mlsanity.com/paper/2205.05173/) (2022)
- [TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection](https://mlsanity.com/paper/2308.11072/) (2023)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement](https://mlsanity.com/paper/2108.01975/) (2021)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [Reconstruction Student with Attention for Student-Teacher Pyramid Matching](https://mlsanity.com/paper/2111.15376/) (2021)
- [Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2201.00464/) (2022)
- [Video Anomaly Detection in 10 Years: A Survey and Outlook](https://mlsanity.com/paper/2405.19387/) (2024)
- [EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition](https://mlsanity.com/paper/2102.03777/) (2021)
- [Deep Video Anomaly Detection: Opportunities and Challenges](https://mlsanity.com/paper/2110.05086/) (2021)
- [Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2312.15911/) (2023)
- [TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera](https://mlsanity.com/paper/2108.12196/) (2021)
- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset](https://mlsanity.com/paper/2211.15513/) (2022)
- [Unsupervised Anomaly Detection from Time-of-Flight Depth Images](https://mlsanity.com/paper/2203.01052/) (2022)
- [Overlooked Video Classification in Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2210.06688/) (2022)
- [Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection](https://mlsanity.com/paper/2107.13304/) (2021)
- [Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning](https://mlsanity.com/paper/2112.06320/) (2021)
- [AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection](https://mlsanity.com/paper/2203.15195/) (2022)
- [Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2302.05154/) (2023)
- [Anomaly detection with semi-supervised classification based on risk estimators](https://mlsanity.com/paper/2309.00379/) (2023)
- [ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification](https://mlsanity.com/paper/2512.03101/) (2025)

## Abstract (arXiv)

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.