mlsanityData to 7 Oct 2026

Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

arXiv 2408.14329 ↗First posted 26 Aug 2024cs.CV cs.AIcited by 6

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Abstract (arXiv)

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5x more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://github.com/TeCSAR-UNCC/HuVAD.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 45 of its 85 references that are in the map. First posted on arXiv 26 Aug 2024.

Builds on

45 of 85 references.

References · 45

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  3. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  4. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  5. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  6. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  7. 2018Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselinescited by 228
  8. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
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  11. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
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33 more
  1. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  2. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  3. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
  4. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  5. 2024Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Modelscited by 55
  6. 2020Continual Learning for Anomaly Detection in Surveillance Videoscited by 49
  7. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  8. 2024Hawk: Learning to Understand Open-World Video Anomaliescited by 47
  9. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  10. 2024Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomalycited by 44
  11. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  12. 2023Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 42
  13. 2023Continual Learning with Scaled Gradient Projectioncited by 33
  14. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
  15. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  16. 2023Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Modelcited by 25
  17. 2020Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Ratecited by 22
  18. 2021Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detectioncited by 22
  19. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 21
  20. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  21. 2024An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Predictioncited by 19
  22. 2022CHAD: Charlotte Anomaly Datasetcited by 18
  23. 2023Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Thingscited by 17
  24. 2023Understanding the Challenges and Opportunities of Pose-based Anomaly Detectioncited by 14
  25. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  26. 2024Context-aware Video Anomaly Detection in Long-Term Datasetscited by 11
  27. 2020Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challengescited by 9
  28. 2023Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safetycited by 9
  29. 2023Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insightscited by 8
  30. 2022OCFR 2022: Competition on Occluded Face Recognition From Synthetically Generated Structure-Aware Occlusionscited by 7
  31. 2022Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safetycited by 7
  32. 2024Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deploymentcited by 7
  33. 2023Exploring Public's Perception of Safety and Video Surveillance Technology: A Survey Approachcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  2. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  3. 2026Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wildcited by 1
  4. 2026From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detectioncited by 1
  5. 2026Structured Evidence Selection for Weakly Supervised Video Anomaly Detectioncited by 0
  6. 2026VQ-VAD: Vector-quantized Motion Representation Learning for Human-centric Video Anomaly Detectioncited by 0