# Continual Learning for Anomaly Detection in Surveillance Videos

arXiv: https://arxiv.org/abs/2004.07941 · HTML: https://mlsanity.com/paper/2004.07941/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 30 of 49)

- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection](https://mlsanity.com/paper/2207.08003/) (2022)
- [Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors](https://mlsanity.com/paper/2306.12041/) (2023)
- [Learning Not to Reconstruct Anomalies](https://mlsanity.com/paper/2110.09742/) (2021)
- [Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization](https://mlsanity.com/paper/2211.10528/) (2022)
- [EVAL: Explainable Video Anomaly Localization](https://mlsanity.com/paper/2212.07900/) (2022)
- [Learning Appearance-motion Normality for Video Anomaly Detection](https://mlsanity.com/paper/2207.13361/) (2022)
- [VideoMiner: Iteratively Grounding Key Frames of Hour-Long Videos via Tree-based Group Relative Policy Optimization](https://mlsanity.com/paper/2510.06040/) (2025)
- [Image/Video Deep Anomaly Detection: A Survey](https://mlsanity.com/paper/2103.01739/) (2021)
- [Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach](https://mlsanity.com/paper/2311.16514/) (2023)
- [Continual Learning on the Edge with TensorFlow Lite](https://mlsanity.com/paper/2105.01946/) (2021)
- [Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems](https://mlsanity.com/paper/2204.03141/) (2022)
- [Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding](https://mlsanity.com/paper/2603.17307/) (2026)
- [Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights](https://mlsanity.com/paper/2303.07557/) (2023)
- [PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies](https://mlsanity.com/paper/2303.10704/) (2023)
- [Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline](https://mlsanity.com/paper/2506.05175/) (2025)
- [A Modular and Unified Framework for Detecting and Localizing Video Anomalies](https://mlsanity.com/paper/2103.11299/) (2021)
- [Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark](https://mlsanity.com/paper/2408.14329/) (2024)
- [Vision-Language Models Assisted Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2409.14109/) (2024)
- [An Efficient Approach for Anomaly Detection in Traffic Videos](https://mlsanity.com/paper/2104.09758/) (2021)
- [Continual learning on 3D point clouds with random compressed rehearsal](https://mlsanity.com/paper/2205.08013/) (2022)
- [Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models](https://mlsanity.com/paper/2206.06957/) (2022)
- [Few-shot Online Anomaly Detection and Segmentation](https://mlsanity.com/paper/2403.18201/) (2024)
- [Certified Continual Learning for Neural Network Regression](https://mlsanity.com/paper/2407.06697/) (2024)
- [On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients](https://mlsanity.com/paper/2411.07959/) (2024)
- [Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies](https://mlsanity.com/paper/2103.10502/) (2021)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2510.14896/) (2025)
- [CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation](https://mlsanity.com/paper/2401.08940/) (2024)
- [ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds](https://mlsanity.com/paper/2508.09058/) (2025)

## 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.