mlsanityData to 14 Oct 2026

Anomaly Detection in Video Sequence with Appearance-Motion Correspondence

arXiv 1908.06351 ↗First posted 17 Aug 2019cs.CV cs.LG cs.NEcited by 118

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Abstract (arXiv)

Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.

Key facts. Cited by 118 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 27; 2022: 35; 2023: 23; 2024: 18; 2025: 10; 2026: 5.

118 papers.

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  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  2. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  3. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  4. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  5. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  6. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 93
  7. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  8. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  9. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  10. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  11. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 60
  12. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
38 more
  1. 2023Video Event Restoration Based on Keyframes for Video Anomaly Detectioncited by 54
  2. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  3. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  4. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  5. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  6. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  7. 2021Learning Not to Reconstruct Anomaliescited by 31
  8. 2022Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detectioncited by 27
  9. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  10. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  11. 2022DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Networkcited by 26
  12. 2021Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detectioncited by 22
  13. 2023BatchNorm-based Weakly Supervised Video Anomaly Detectioncited by 22
  14. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 22
  15. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  16. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  17. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  18. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  19. 2023Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representationscited by 19
  20. 2022Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detectioncited by 18
  21. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  22. 2024Law of Vision Representation in MLLMscited by 18
  23. 2021Learnable Locality-Sensitive Hashing for Video Anomaly Detectioncited by 17
  24. 2021FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformationcited by 15
  25. 2021OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detectioncited by 14
  26. 2022Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomaliescited by 14
  27. 2022Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videoscited by 12
  28. 2024Context-aware Video Anomaly Detection in Long-Term Datasetscited by 11
  29. 2021Image/Video Deep Anomaly Detection: A Surveycited by 10
  30. 2023Making Reconstruction-based Method Great Again for Video Anomaly Detectioncited by 10
  31. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  32. 2025A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Predictioncited by 10
  33. 2022Adversarial Machine Learning Attacks Against Video Anomaly Detection Systemscited by 9
  34. 2024Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 9
  35. 2024Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomalycited by 9
  36. 2024STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detectioncited by 9
  37. 2022Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detectioncited by 8
  38. 2023Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistencycited by 8

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