mlsanityData to 7 Oct 2026

Networking Systems for Video Anomaly Detection: A Tutorial and Survey

arXiv 2405.10347 ↗First posted 16 May 2024cs.CV cs.AI cs.CYcited by 21

Not on the map. Why?

Abstract (arXiv)

The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. In addition, this article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 71 of its 218 references that are in the map. First posted on arXiv 16 May 2024.

Builds on

71 of 218 references.

References · 71

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  5. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  6. 2015Fast R-CNNcited by 2,930
  7. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  8. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  9. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
  10. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  11. 2022Multimodal Learning with Transformers: A Surveycited by 399
  12. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 396
38 more
  1. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  2. 2018Recent Advances in Open Set Recognition: A Surveycited by 309
  3. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  4. 2016Learning Temporal Regularity in Video Sequencescited by 254
  5. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  6. 2018Human Action Recognition and Prediction: A Surveycited by 226
  7. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  8. 2020A review on outlier/anomaly detection in time series datacited by 187
  9. 2021PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learningcited by 181
  10. 2020Human Action Recognition from Various Data Modalities: A Reviewcited by 162
  11. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  12. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 147
  13. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  14. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  15. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  16. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  17. 2020A Review on Deep Learning Techniques for Video Predictioncited by 114
  18. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  19. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  20. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  21. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 85
  22. 2020Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detectioncited by 83
  23. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  24. 2020Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Eventscited by 70
  25. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  26. 2017Abnormal Event Detection in Videos using Spatiotemporal Autoencodercited by 57
  27. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  28. 2022Multimodal learning with graphscited by 54
  29. 2023Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perceptioncited by 52
  30. 2018An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videoscited by 45
  31. 2020Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Predictioncited by 42
  32. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  33. 2022A Survey on Explainable Anomaly Detectioncited by 39
  34. 2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
  35. 2016Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detectioncited by 32
  36. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  37. 2019Anomaly Detection in Road Traffic Using Visual Surveillance: A Surveycited by 30
  38. 2017Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowdscited by 28

50 most cited of 71 All 71 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 10.

21 papers.

Sort:

By area:

  1. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  2. 2025A Survey on Diffusion Models for Anomaly Detectioncited by 13
  3. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  4. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  5. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  6. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  7. 2024Long-term Pre-training for Temporal Action Detection with Transformerscited by 3
  8. 2026Collaborative Adaptive Curriculum for Progressive Knowledge Distillationcited by 2
  9. 2025Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddingscited by 1
  10. 2025M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detectioncited by 1
  11. 2026MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detectioncited by 1
  12. 2024End-Cloud Collaboration Framework for Advanced AI Customer Service in E-commercecited by 0
9 more
  1. 2025Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labelingcited by 0
  2. 2026Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classificationcited by 0
  3. 2026Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detectioncited by 0
  4. 2026Reliability-Aware Prototype Calibration for Frozen Pose-Flow Video Anomaly Detectioncited by 0
  5. 2026Structured Evidence Selection for Weakly Supervised Video Anomaly Detectioncited by 0
  6. 2026PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMscited by 0
  7. 2026Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuningcited by 0
  8. 2026GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignmentcited by 0
  9. 2026Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restorationcited by 0