mlsanityData to 7 Oct 2026

Continual Learning for Anomaly Detection in Surveillance Videos

arXiv 2004.07941 ↗First posted 15 Apr 2020cs.CV cs.LG eess.IVcited by 49

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Abstract (arXiv)

Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning. While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learning framework due to computational and storage issues. Furthermore, online decision making is an important but mostly neglected factor in this domain. Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and continual learning, which in turn significantly reduces the training complexity and provides a mechanism for continually learning from recent data without suffering from catastrophic forgetting. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning, and the continual learning capability of statistical detection methods.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2020.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 5; 2024: 10; 2025: 10; 2026: 4.

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  1. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  2. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  3. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  4. 2021Learning Not to Reconstruct Anomaliescited by 31
  5. 2022Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localizationcited by 24
  6. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  7. 2022Learning Appearance-motion Normality for Video Anomaly Detectioncited by 17
  8. 2021Image/Video Deep Anomaly Detection: A Surveycited by 10
  9. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  10. 2021Continual Learning on the Edge with TensorFlow Litecited by 9
  11. 2022Adversarial Machine Learning Attacks Against Video Anomaly Detection Systemscited by 9
  12. 2025VideoMiner: Iteratively Grounding Key Frames of Hour-Long Videos via Tree-based Group Relative Policy Optimizationcited by 9
37 more
  1. 2023Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insightscited by 8
  2. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  3. 2025Track Any Anomalous Object: A Granular Video Anomaly Detection Pipelinecited by 8
  4. 2026Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understandingcited by 8
  5. 2021A Modular and Unified Framework for Detecting and Localizing Video Anomaliescited by 6
  6. 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmarkcited by 6
  7. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  8. 2021An Efficient Approach for Anomaly Detection in Traffic Videoscited by 5
  9. 2022Continual learning on 3D point clouds with random compressed rehearsalcited by 5
  10. 2022Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Modelscited by 5
  11. 2024Few-shot Online Anomaly Detection and Segmentationcited by 5
  12. 2024Certified Continual Learning for Neural Network Regressioncited by 5
  13. 2024On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradientscited by 5
  14. 2021Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomaliescited by 4
  15. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  16. 2025Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detectioncited by 4
  17. 2024CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidationcited by 3
  18. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  19. 2026From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Securitycited by 3
  20. 2021Video Anomaly Detection By The Duality Of Normality-Granted Optical Flowcited by 2
  21. 2021Y-GAN: Learning Dual Data Representations for Efficient Anomaly Detectioncited by 2
  22. 2022Logarithmic Continual Learningcited by 2
  23. 2022Object-centric and memory-guided normality reconstruction for video anomaly detectioncited by 2
  24. 2024Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flowscited by 2
  25. 2022Look at Adjacent Frames: Video Anomaly Detection without Offline Trainingcited by 1
  26. 2024EdgeOL: Efficient in-situ Online Learning on Edge Devicescited by 1
  27. 2025Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Conceptscited by 1
  28. 2021Approaches Toward Physical and General Video Anomaly Detectioncited by 0
  29. 2023Reasonable Anomaly Detection in Long Sequencescited by 0
  30. 2024Towards stable training of parallel continual learningcited by 0
  31. 2024Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysiscited by 0
  32. 2025Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videoscited by 0
  33. 2025Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approachescited by 0
  34. 2025Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Citiescited by 0
  35. 20253D Human-Human Interaction Anomaly Detectioncited by 0
  36. 2026CLeAN: Continual Learning Adaptive Normalization in Dynamic Environmentscited by 0
  37. 2026Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inferencecited by 0