# A Survey of Single-Scene Video Anomaly Detection

arXiv: https://arxiv.org/abs/2004.05993 · HTML: https://mlsanity.com/paper/2004.05993/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 30 of 88)

- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2310.14228/) (2023)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection](https://mlsanity.com/paper/2207.08003/) (2022)
- [Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2306.14451/) (2023)
- [A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation](https://mlsanity.com/paper/2305.13611/) (2023)
- [Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection](https://mlsanity.com/paper/2303.13051/) (2023)
- [Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors](https://mlsanity.com/paper/2306.12041/) (2023)
- [Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection](https://mlsanity.com/paper/2209.12148/) (2022)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Towards Open Set Video Anomaly Detection](https://mlsanity.com/paper/2208.11113/) (2022)
- [Perturbation Learning Based Anomaly Detection](https://mlsanity.com/paper/2206.02704/) (2022)
- [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)
- [EVAL: Explainable Video Anomaly Localization](https://mlsanity.com/paper/2212.07900/) (2022)
- [Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection](https://mlsanity.com/paper/2209.02899/) (2022)
- [Video Anomaly Detection in 10 Years: A Survey and Outlook](https://mlsanity.com/paper/2405.19387/) (2024)
- [Learnable Locality-Sensitive Hashing for Video Anomaly Detection](https://mlsanity.com/paper/2111.07839/) (2021)
- [FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation](https://mlsanity.com/paper/2106.08613/) (2021)
- [Deep Video Anomaly Detection: Opportunities and Challenges](https://mlsanity.com/paper/2110.05086/) (2021)
- [LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detection](https://mlsanity.com/paper/2307.08059/) (2023)
- [SOAR: Scene-debiasing Open-set Action Recognition](https://mlsanity.com/paper/2309.01265/) (2023)
- [A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysis](https://mlsanity.com/paper/2511.00962/) (2025)
- [Context-aware Video Anomaly Detection in Long-Term Datasets](https://mlsanity.com/paper/2404.07887/) (2024)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach](https://mlsanity.com/paper/2311.16514/) (2023)

## Abstract (arXiv)

This survey article summarizes research trends on the topic of anomaly detection in video feeds of a single scene. We discuss the various problem formulations, publicly available datasets and evaluation criteria. We categorize and situate past research into an intuitive taxonomy and provide a comprehensive comparison of the accuracy of many algorithms on standard test sets. Finally, we also provide best practices and suggest some possible directions for future research.