mlsanityData to 14 Oct 2026

Deep Learning for Video Anomaly Detection: A Review

arXiv 2409.05383 ↗First posted 9 Sep 2024cs.CV cs.AIcited by 27

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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.

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.

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124 of 258 references.

References · 124

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  4. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  5. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 902
  6. 2016CNN Architectures for Large-Scale Audio Classificationcited by 704
  7. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  8. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  9. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  10. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  11. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  12. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
38 more
  1. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  2. 2016Learning Temporal Regularity in Video Sequencescited by 254
  3. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 238
  4. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  5. 2023Class-Incremental Learning: A Surveycited by 209
  6. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
  7. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  8. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  9. 2020Human Action Recognition from Various Data Modalities: A Reviewcited by 163
  10. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 148
  11. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 138
  12. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  13. 2020Explainable Deep One-Class Classificationcited by 127
  14. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 123
  15. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  16. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  17. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  18. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 111
  19. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 104
  20. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  21. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  22. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  23. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 93
  24. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 90
  25. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 90
  26. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 89
  27. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 89
  28. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  29. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 87
  30. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 86
  31. 2020Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detectioncited by 83
  32. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  33. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  34. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  35. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  36. 2020Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Eventscited by 70
  37. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  38. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64

50 most cited of 124 All 124 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 14; 2026: 10.

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  1. 2025VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuningcited by 24
  2. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  3. 2025PANDA: Towards Generalist Video Anomaly Detection via Agentic AI Engineercited by 19
  4. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  5. 2025Language-guided Open-world Video Anomaly Detection under Weak Supervisioncited by 8
  6. 2025AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detectioncited by 7
  7. 2025SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Modelcited by 6
  8. 2026Failure Identification in Imitation Learning Via Statistical and Semantic Filteringcited by 6
  9. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  10. 2025Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detectioncited by 4
  11. 2025Rethinking Metrics and Benchmarks of Video Anomaly Detectioncited by 3
  12. 2025Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challengescited by 3
15 more
  1. 2025Video Anomaly Detection with Structured Keywordscited by 2
  2. 2025Text-guided Fine-Grained Video Anomaly Understandingcited by 2
  3. 2025TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detectioncited by 1
  4. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  5. 2026ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitionscited by 1
  6. 2026AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detectioncited by 1
  7. 2026Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detectioncited by 1
  8. 2024Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenescited by 0
  9. 2025Normality Calibration in Semi-supervised Graph Anomaly Detectioncited by 0
  10. 2025TorchTraceAP: A New Benchmark Dataset for Detecting Performance Anti-Patterns in Computer Vision Modelscited by 0
  11. 2026Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classificationcited by 0
  12. 2026Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Visioncited by 0
  13. 2026InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentationcited by 0
  14. 2026SynCrash: A Multi-Stage Pipeline for Zero-Shot Accident Detection and Localization in Traffic Surveillance Videocited by 0
  15. 2026EvoSherlock: Towards Agentic Lifelong Evolution for Unseen Long-Tailed Security-Critical Events in Videoscited by 0