# Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection

arXiv: https://arxiv.org/abs/2409.03236 · HTML: https://mlsanity.com/paper/2409.03236/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 86 references that are in the map.
- First posted on arXiv 5 Sep 2024.

## Builds on (most cited first, 30 of 43)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Future Frame Prediction for Anomaly Detection -- A New Baseline](https://mlsanity.com/paper/1712.09867/) (2017)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning](https://mlsanity.com/paper/2101.10030/) (2021)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Graph Embedded Pose Clustering for Anomaly Detection](https://mlsanity.com/paper/1912.11850/) (2019)
- [UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection](https://mlsanity.com/paper/2111.08644/) (2021)
- [Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2302.05160/) (2023)
- [MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection](https://mlsanity.com/paper/2211.15098/) (2022)
- [Learning Normal Dynamics in Videos with Meta Prototype Network](https://mlsanity.com/paper/2104.06689/) (2021)
- [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)
- [Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models](https://mlsanity.com/paper/2407.10299/) (2024)
- [A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation](https://mlsanity.com/paper/2305.13611/) (2023)
- [Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection](https://mlsanity.com/paper/2307.07205/) (2023)
- [Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection](https://mlsanity.com/paper/2303.13051/) (2023)
- [A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos](https://mlsanity.com/paper/2112.04294/) (2021)
- [Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model](https://mlsanity.com/paper/2307.12545/) (2023)
- [Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection](https://mlsanity.com/paper/2112.03649/) (2021)
- [BatchNorm-based Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2311.15367/) (2023)
- [Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features](https://mlsanity.com/paper/2303.15167/) (2023)
- [Depth Quality Aware Salient Object Detection](https://mlsanity.com/paper/2008.04159/) (2020)
- [Learnable Locality-Sensitive Hashing for Video Anomaly Detection](https://mlsanity.com/paper/2111.07839/) (2021)
- [Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention](https://mlsanity.com/paper/2309.16309/) (2023)

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

- [FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization](https://mlsanity.com/paper/2501.10067/) (2025)
- [VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models](https://mlsanity.com/paper/2511.07299/) (2025)
- [HiRQA: Hierarchical Ranking and Quality Alignment for Opinion-Unaware Image Quality Assessment](https://mlsanity.com/paper/2508.15130/) (2025)

## Abstract (arXiv)

Detecting anomalies in human-related videos is crucial for surveillance applications. Current methods primarily include appearance-based and action-based techniques. Appearance-based methods rely on low-level visual features such as color, texture, and shape. They learn a large number of pixel patterns and features related to known scenes during training, making them effective in detecting anomalies within these familiar contexts. However, when encountering new or significantly changed scenes, i.e., unknown scenes, they often fail because existing SOTA methods do not effectively capture the relationship between actions and their surrounding scenes, resulting in low generalization. In contrast, action-based methods focus on detecting anomalies in human actions but are usually less informative because they tend to overlook the relationship between actions and their scenes, leading to incorrect detection. For instance, the normal event of running on the beach and the abnormal event of running on the street might both be considered normal due to the lack of scene information. In short, current methods struggle to integrate low-level visual and high-level action features, leading to poor anomaly detection in varied and complex scenes. To address this challenge, we propose a novel decoupling-based architecture for human-related video anomaly detection (DecoAD). DecoAD significantly improves the integration of visual and action features through the decoupling and interweaving of scenes and actions, thereby enabling a more intuitive and accurate understanding of complex behaviors and scenes. DecoAD supports fully supervised, weakly supervised, and unsupervised settings.