# Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate

arXiv: https://arxiv.org/abs/2010.07110 · HTML: https://mlsanity.com/paper/2010.07110/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2020.

## Built on by (most cited first, 22 of 22)

- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement](https://mlsanity.com/paper/2108.01975/) (2021)
- [Networking Systems for Video Anomaly Detection: A Tutorial and Survey](https://mlsanity.com/paper/2405.10347/) (2024)
- [MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection](https://mlsanity.com/paper/2510.21449/) (2025)
- [Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems](https://mlsanity.com/paper/2204.03141/) (2022)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment](https://mlsanity.com/paper/2404.18747/) (2024)
- [Learning Representation for Anomaly Detection of Vehicle Trajectories](https://mlsanity.com/paper/2303.05000/) (2023)
- [Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection](https://mlsanity.com/paper/2404.04986/) (2024)
- [Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark](https://mlsanity.com/paper/2408.14329/) (2024)
- [An Efficient Approach for Anomaly Detection in Traffic Videos](https://mlsanity.com/paper/2104.09758/) (2021)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [Crime scene classification from skeletal trajectory analysis in surveillance settings](https://mlsanity.com/paper/2207.01687/) (2022)
- [ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds](https://mlsanity.com/paper/2508.09058/) (2025)
- [From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Security](https://mlsanity.com/paper/2603.04723/) (2026)
- [Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests](https://mlsanity.com/paper/2108.02356/) (2021)
- [TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model](https://mlsanity.com/paper/2201.02941/) (2022)
- [uTRAND: Unsupervised Anomaly Detection in Traffic Trajectories](https://mlsanity.com/paper/2404.12712/) (2024)
- [Dual Conditioned Motion Diffusion for Pose-Based Video Anomaly Detection](https://mlsanity.com/paper/2412.17210/) (2024)
- [From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection](https://mlsanity.com/paper/2604.09327/) (2026)
- [Depth-Weighted Detection of Behaviours of Risk in People with Dementia using Cameras](https://mlsanity.com/paper/2408.15519/) (2024)
- [Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysis](https://mlsanity.com/paper/2410.15909/) (2024)

## Abstract (arXiv)

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of recent methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, online decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate. Our proposed algorithm consists of a multi-objective deep learning module along with a statistical anomaly detection module, and its effectiveness is demonstrated on several publicly available data sets where we outperform the state-of-the-art algorithms. All codes are available at https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-.