# EVAL: Explainable Video Anomaly Localization

arXiv: https://arxiv.org/abs/2212.07900 · HTML: https://mlsanity.com/paper/2212.07900/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 23 of 23)

- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Learning Memory-guided Normality for Anomaly Detection](https://mlsanity.com/paper/2003.13228/) (2020)
- [Learning Temporal Regularity in Video Sequences](https://mlsanity.com/paper/1604.04574/) (2016)
- [Anomaly Detection in Video via Self-Supervised and Multi-Task Learning](https://mlsanity.com/paper/2011.07491/) (2020)
- [A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction](https://mlsanity.com/paper/2108.06852/) (2021)
- [Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection](https://mlsanity.com/paper/2111.09099/) (2021)
- [Anomaly Detection in Video Sequence with Appearance-Motion Correspondence](https://mlsanity.com/paper/1908.06351/) (2019)
- [Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video](https://mlsanity.com/paper/1812.04960/) (2018)
- [Street Scene: A new dataset and evaluation protocol for video anomaly detection](https://mlsanity.com/paper/1902.05872/) (2019)
- [A Survey of Single-Scene Video Anomaly Detection](https://mlsanity.com/paper/2004.05993/) (2020)
- [A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video](https://mlsanity.com/paper/2008.12328/) (2020)
- [Abnormal Event Detection in Videos using Generative Adversarial Nets](https://mlsanity.com/paper/1708.09644/) (2017)
- [Learning Deep Representations of Appearance and Motion for Anomalous Event Detection](https://mlsanity.com/paper/1510.01553/) (2015)
- [Few-shot Scene-adaptive Anomaly Detection](https://mlsanity.com/paper/2007.07843/) (2020)
- [Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge](https://mlsanity.com/paper/1709.09121/) (2017)
- [Continual Learning for Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.07941/) (2020)
- [Any-Shot Sequential Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/2004.02072/) (2020)
- [Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection](https://mlsanity.com/paper/1610.00307/) (2016)
- [Learning a distance function with a Siamese network to localize anomalies in videos](https://mlsanity.com/paper/2001.09189/) (2020)
- [Detecting abnormal events in video using Narrowed Normality Clusters](https://mlsanity.com/paper/1801.05030/) (2018)
- [Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection](https://mlsanity.com/paper/2110.09768/) (2021)
- [An Efficient Approach for Anomaly Detection in Traffic Videos](https://mlsanity.com/paper/2104.09758/) (2021)

## Built on by (most cited first, 19 of 19)

- [Deep Learning for Video Anomaly Detection: A Review](https://mlsanity.com/paper/2409.05383/) (2024)
- [Video Anomaly Detection in 10 Years: A Survey and Outlook](https://mlsanity.com/paper/2405.19387/) (2024)
- [Context-aware Video Anomaly Detection in Long-Term Datasets](https://mlsanity.com/paper/2404.07887/) (2024)
- [Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach](https://mlsanity.com/paper/2311.16514/) (2023)
- [ComplexVAD: Detecting Interaction Anomalies in Video](https://mlsanity.com/paper/2501.09733/) (2025)
- [Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videos](https://mlsanity.com/paper/2302.06670/) (2023)
- [Vision-Language Models Assisted Unsupervised Video Anomaly Detection](https://mlsanity.com/paper/2409.14109/) (2024)
- [Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection](https://mlsanity.com/paper/2412.03044/) (2024)
- [Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2510.14896/) (2025)
- [Detecting Contextual Anomalies by Discovering Consistent Spatial Regions](https://mlsanity.com/paper/2501.08470/) (2025)
- [Bounding-Box Trajectories Matter for Video Anomaly Detection](https://mlsanity.com/paper/2605.21957/) (2026)
- [Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach](https://mlsanity.com/paper/2309.14622/) (2023)
- [An Integrated Framework for Multi-Granular Explanation of Video Summarization](https://mlsanity.com/paper/2405.10082/) (2024)
- [Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified](https://mlsanity.com/paper/2407.06000/) (2024)
- [Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection](https://mlsanity.com/paper/2410.03904/) (2024)
- [Video Anomaly Detection with Contours -- A Study](https://mlsanity.com/paper/2503.19588/) (2025)
- [An Experimental Study on Generating Plausible Textual Explanations for Video Summarization](https://mlsanity.com/paper/2509.26225/) (2025)
- [Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference](https://mlsanity.com/paper/2604.13252/) (2026)
- [GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturing](https://mlsanity.com/paper/2608.22789/) (2026)

## Abstract (arXiv)

We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art.