# Deep Learning for Video Anomaly Detection: A Review

arXiv: https://arxiv.org/abs/2409.05383 · HTML: https://mlsanity.com/paper/2409.05383/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 124 of its 258 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 30 of 124)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation](https://mlsanity.com/paper/2303.14814/) (2023)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision](https://mlsanity.com/paper/2007.04687/) (2020)
- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [Class-Incremental Learning: A Survey](https://mlsanity.com/paper/2302.03648/) (2023)
- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection](https://mlsanity.com/paper/1903.07256/) (2019)
- [Human Action Recognition from Various Data Modalities: A Review](https://mlsanity.com/paper/2012.11866/) (2020)
- [Anomaly Detection in Video via Self-Supervised and Multi-Task Learning](https://mlsanity.com/paper/2011.07491/) (2020)
- [MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection](https://mlsanity.com/paper/2104.01633/) (2021)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2308.11681/) (2023)
- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [Anomaly Detection in Video Sequence with Appearance-Motion Correspondence](https://mlsanity.com/paper/1908.06351/) (2019)
- [Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm](https://mlsanity.com/paper/2004.07657/) (2020)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)

## Built on by (most cited first, 27 of 27)

- [VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2505.23504/) (2025)
- [Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2410.10289/) (2024)
- [PANDA: Towards Generalist Video Anomaly Detection via Agentic AI Engineer](https://mlsanity.com/paper/2509.26386/) (2025)
- [Privacy-Preserving Video Anomaly Detection: A Survey](https://mlsanity.com/paper/2411.14565/) (2024)
- [Language-guided Open-world Video Anomaly Detection under Weak Supervision](https://mlsanity.com/paper/2503.13160/) (2025)
- [AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection](https://mlsanity.com/paper/2504.04495/) (2025)
- [SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model](https://mlsanity.com/paper/2504.10320/) (2025)
- [Failure Identification in Imitation Learning Via Statistical and Semantic Filtering](https://mlsanity.com/paper/2604.13788/) (2026)
- [The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM](https://mlsanity.com/paper/2507.21649/) (2025)
- [Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2510.14896/) (2025)
- [Rethinking Metrics and Benchmarks of Video Anomaly Detection](https://mlsanity.com/paper/2505.19022/) (2025)
- [Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges](https://mlsanity.com/paper/2507.02074/) (2025)
- [Video Anomaly Detection with Structured Keywords](https://mlsanity.com/paper/2503.10653/) (2025)
- [Text-guided Fine-Grained Video Anomaly Understanding](https://mlsanity.com/paper/2511.00524/) (2025)
- [TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detection](https://mlsanity.com/paper/2511.00580/) (2025)
- [Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision](https://mlsanity.com/paper/2603.13306/) (2026)
- [ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions](https://mlsanity.com/paper/2604.07772/) (2026)
- [AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection](https://mlsanity.com/paper/2605.30140/) (2026)
- [Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detection](https://mlsanity.com/paper/2609.17211/) (2026)
- [Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenes](https://mlsanity.com/paper/2412.20363/) (2024)
- [Normality Calibration in Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2510.02014/) (2025)
- [TorchTraceAP: A New Benchmark Dataset for Detecting Performance Anti-Patterns in Computer Vision Models](https://mlsanity.com/paper/2512.14141/) (2025)
- [Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification](https://mlsanity.com/paper/2601.06204/) (2026)
- [Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision](https://mlsanity.com/paper/2602.23514/) (2026)
- [InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation](https://mlsanity.com/paper/2604.04632/) (2026)
- [SynCrash: A Multi-Stage Pipeline for Zero-Shot Accident Detection and Localization in Traffic Surveillance Video](https://mlsanity.com/paper/2608.29759/) (2026)
- [EvoSherlock: Towards Agentic Lifelong Evolution for Unseen Long-Tailed Security-Critical Events in Videos](https://mlsanity.com/paper/2609.19201/) (2026)

## Abstract (arXiv)

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 paper, 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 pre-trained large models, 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.