mlsanityData to 7 Oct 2026

Unmasking the abnormal events in video

arXiv 1705.08182 ↗First posted 23 May 2017cs.CVcited by 48

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Abstract (arXiv)

We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 12; 2023: 5; 2024: 9; 2025: 3; 2026: 2.

48 papers.

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  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  2. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  3. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  4. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  5. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  6. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  7. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  8. 2023Video Event Restoration Based on Keyframes for Video Anomaly Detectioncited by 54
  9. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  10. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  11. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  12. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
36 more
  1. 2022Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detectioncited by 27
  2. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  3. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  4. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  5. 2022Learning Appearance-motion Normality for Video Anomaly Detectioncited by 17
  6. 2022Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomaliescited by 14
  7. 2021Anomaly Detection in Video Sequences: A Benchmark and Computational Modelcited by 10
  8. 2021Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warpingcited by 7
  9. 2021A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methodscited by 7
  10. 2025Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detectioncited by 7
  11. 2024Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detectioncited by 5
  12. 2024ProMotion: Prototypes As Motion Learnerscited by 5
  13. 2021Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomaliescited by 4
  14. 2024Prototypical Transformer as Unified Motion Learnerscited by 4
  15. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  16. 2022Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillationcited by 3
  17. 2023Human-centric Behavior Description in Videos: New Benchmark and Modelcited by 3
  18. 2026Bounding-Box Trajectories Matter for Video Anomaly Detectioncited by 3
  19. 2021Discriminative-Generative Dual Memory Video Anomaly Detectioncited by 2
  20. 2021Video Anomaly Detection By The Duality Of Normality-Granted Optical Flowcited by 2
  21. 2024Multiple Instance Learning for Cheating Detection and Localization in Online Examinationscited by 2
  22. 2024Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignmentcited by 2
  23. 2024CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly Detectioncited by 2
  24. 2021Anomaly Detection with Prototype-Guided Discriminative Latent Embeddingscited by 1
  25. 2021Video Abnormal Event Detection by Learning to Complete Visual Cloze Testscited by 1
  26. 2021CPNet: Cross-Parallel Network for Efficient Anomaly Detectioncited by 1
  27. 2022Look at Adjacent Frames: Video Anomaly Detection without Offline Trainingcited by 1
  28. 2022Spatio-Temporal Relation Learning for Video Anomaly Detectioncited by 1
  29. 2021"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networkscited by 0
  30. 2021Importance Weighted Adversarial Discriminative Transfer for Anomaly Detectioncited by 0
  31. 2021Normal Learning in Videos with Attention Prototype Networkcited by 0
  32. 2022Hybrid Classifiers for Spatio-temporal Real-time Abnormal Behaviors Detection, Tracking, and Recognition in Massive Hajj Crowdscited by 0
  33. 2024Patch Spatio-Temporal Relation Prediction for Video Anomaly Detectioncited by 0
  34. 2024Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoringcited by 0
  35. 2025Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approachescited by 0
  36. 2026STEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detectioncited by 0