mlsanityData to 13 Oct 2026

Future Frame Prediction for Anomaly Detection -- A New Baseline

arXiv 1712.09867 ↗First posted 28 Dec 2017cs.CVcited by 391

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Abstract (arXiv)

Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events.

Key facts. Cited by 391 papers in the map (data through 13 Oct 2026). First posted on arXiv 28 Dec 2017.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 68; 2022: 65; 2023: 66; 2024: 74; 2025: 62; 2026: 56.

391 papers.

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  1. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,535
  2. 2021Generalized Out-of-Distribution Detection: A Surveycited by 799
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 774
  4. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 511
  5. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 420
  6. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 228
  7. 2023A Survey on Video Diffusion Modelscited by 206
  8. 2021Anticipative Video Transformercited by 199
  9. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  10. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  11. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 123
  12. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
38 more
  1. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  2. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 92
  3. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 90
  4. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  5. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  6. 2024Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularitycited by 68
  7. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  8. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  9. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 64
  10. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  11. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  12. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  13. 2023Open-Vocabulary Video Anomaly Detectioncited by 57
  14. 2022Prototypical Residual Networks for Anomaly Detection and Localizationcited by 56
  15. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  16. 2024Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Modelscited by 55
  17. 2023Video Event Restoration Based on Keyframes for Video Anomaly Detectioncited by 54
  18. 2021TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wildcited by 52
  19. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  20. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  21. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  22. 2024Hawk: Learning to Understand Open-World Video Anomaliescited by 47
  23. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  24. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  25. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  26. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  27. 2023Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 42
  28. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  29. 2023Diversity-Measurable Anomaly Detectioncited by 41
  30. 2021Learning to Anticipate Egocentric Actions by Imaginationcited by 40
  31. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
  32. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  33. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  34. 2021Learning with Multiclass AUC: Theory and Algorithmscited by 35
  35. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  36. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  37. 2021Learning Not to Reconstruct Anomaliescited by 31
  38. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28

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