# ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds

arXiv: https://arxiv.org/abs/2508.09058 · HTML: https://mlsanity.com/paper/2508.09058/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 44 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 28 of 28)

- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Anomaly Detection in Video via Self-Supervised and Multi-Task Learning](https://mlsanity.com/paper/2011.07491/) (2020)
- [Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos](https://mlsanity.com/paper/1903.03295/) (2019)
- [Generative Cooperative Learning for Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2203.03962/) (2022)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video](https://mlsanity.com/paper/2008.12328/) (2020)
- [BMAD: Benchmarks for Medical Anomaly Detection](https://mlsanity.com/paper/2306.11876/) (2023)
- [Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles](https://mlsanity.com/paper/2207.10172/) (2022)
- [Normalizing Flows for Human Pose Anomaly Detection](https://mlsanity.com/paper/2211.10946/) (2022)
- [Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection](https://mlsanity.com/paper/1908.04321/) (2019)
- [Continual Learning for Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.07941/) (2020)
- [SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection](https://mlsanity.com/paper/2207.08003/) (2022)
- [A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos](https://mlsanity.com/paper/2112.04294/) (2021)
- [Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate](https://mlsanity.com/paper/2010.07110/) (2020)
- [Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection](https://mlsanity.com/paper/2112.03649/) (2021)
- [An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction](https://mlsanity.com/paper/2406.15395/) (2024)
- [CHAD: Charlotte Anomaly Dataset](https://mlsanity.com/paper/2212.09258/) (2022)
- [Understanding the Challenges and Opportunities of Pose-based Anomaly Detection](https://mlsanity.com/paper/2303.05463/) (2023)
- [Anomaly Detection with Domain Adaptation](https://mlsanity.com/paper/2006.03689/) (2020)
- [Context-aware Video Anomaly Detection in Long-Term Datasets](https://mlsanity.com/paper/2404.07887/) (2024)
- [CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention](https://mlsanity.com/paper/2404.18952/) (2024)
- [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)
- [Multi-Task Learning based Video Anomaly Detection with Attention](https://mlsanity.com/paper/2210.07697/) (2022)
- [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)
- [Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformer](https://mlsanity.com/paper/2408.15185/) (2024)
- [Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection](https://mlsanity.com/paper/2303.05116/) (2023)
- [Exploring Public's Perception of Safety and Video Surveillance Technology: A Survey Approach](https://mlsanity.com/paper/2312.06707/) (2023)

## Built on by (most cited first, 3 of 3)

- [Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild](https://mlsanity.com/paper/2603.04727/) (2026)
- [From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection](https://mlsanity.com/paper/2604.09327/) (2026)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)

## Abstract (arXiv)

Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active learning framework tailored for VAD, designed for adapting to the ever-changing real-world conditions. Our approach leverages active learning to continuously select the most informative data points for labeling, thereby enhancing model adaptability. A critical innovation is the incorporation of a human-in-the-loop mechanism, which enables the identification of actual normal and anomalous instances from pseudo-labeling results generated by AI. This collected data allows the framework to define an adaptive threshold tailored to different environments, ensuring that the system remains effective as the definition of 'normal' shifts across various settings. Implemented within a lab-based framework that simulates real-world conditions, our approach allows rigorous testing and refinement of VAD algorithms with a new metric. Experimental results show that our method achieves an EBI (Error Balance Index) of 68.91 for Q3 in real-world simulated scenarios, demonstrating its practical effectiveness and significantly enhancing the applicability of VAD in dynamic environments.