mlsanityData to 14 Oct 2026

Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

arXiv 2301.00114 ↗First posted 31 Dec 2022cs.CVcited by 14

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Abstract (arXiv)

The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 33 of its 110 references that are in the map. First posted on arXiv 31 Dec 2022.

Builds on

33 of 110 references.

References · 33

  1. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  2. 2018YOLOv3: An Incremental Improvementcited by 2,659
  3. 2017The Kinetics Human Action Video Datasetcited by 1,909
  4. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  5. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  6. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  7. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  8. 2019MediaPipe: A Framework for Building Perception Pipelinescited by 583
  9. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  10. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  11. 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 533
  12. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
21 more
  1. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  2. 2022ActionFormer: Localizing Moments of Actions with Transformerscited by 311
  3. 20213D Human Pose Estimation with Spatial and Temporal Transformerscited by 272
  4. 2017Towards Accurate Multi-person Pose Estimation in the Wildcited by 131
  5. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 104
  6. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  7. 2013One-Class Classification: Taxonomy of Study and Review of Techniquescited by 68
  8. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
  9. 2020Monocular Human Pose Estimation: A Survey of Deep Learning-based Methodscited by 59
  10. 2020Driver Anomaly Detection: A Dataset and Contrastive Learning Approachcited by 41
  11. 2019RWF-2000: An Open Large Scale Video Database for Violence Detectioncited by 39
  12. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
  13. 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
  14. 2016Multi-Person Pose Estimation with Local Joint-to-Person Associationscited by 18
  15. 2022Privacy-Protecting Behaviours of Risk Detection in People with Dementia using Videoscited by 8
  16. 2020Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imagingcited by 7
  17. 2022Multi Visual Modality Fall Detection Datasetcited by 7
  18. 2022Unsupervised Anomaly Detection from Time-of-Flight Depth Imagescited by 6
  19. 2021HR-Crime: Human-Related Anomaly Detection in Surveillance Videoscited by 5
  20. 2015Detecting Falls with X-Factor Hidden Markov Modelscited by 3
  21. 2020Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classificationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 3; 2026: 3.

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  1. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 49
  2. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  3. 2024Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connectionscited by 11
  4. 2024Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 9
  5. 2024Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detectioncited by 6
  6. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  7. 2024Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detectioncited by 3
  8. 2025Learning by Neighbor-Aware Semantics, Deciding by Open-form Flows: Towards Robust Zero-Shot Skeleton Action Recognitioncited by 3
  9. 2025Emotion Recognition from Skeleton Data: A Comprehensive Surveycited by 2
  10. 2026MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarioscited by 2
  11. 2023Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encodercited by 1
  12. 2026From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detectioncited by 1
2 more
  1. 2024Depth-Weighted Detection of Behaviours of Risk in People with Dementia using Camerascited by 0
  2. 2026BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detectioncited by 0