mlsanityData to 7 Oct 2026

Motion-Aware Feature for Improved Video Anomaly Detection

arXiv 1907.10211 ↗First posted 24 Jul 2019cs.CV cs.LG eess.IVcited by 48

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Abstract (arXiv)

Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feature. This feature alone can achieve competitive performance with previous state-of-the-art methods, and when combined with them, can achieve significant performance improvements. Furthermore, we incorporate temporal context into the Multiple Instance Learning (MIL) ranking model by using an attention block. The learned attention weights can help to differentiate between anomalous and normal video segments better. With the proposed motion-aware feature and the temporal MIL ranking model, we outperform previous approaches by a large margin on both anomaly detection and anomalous action recognition tasks in the UCF Crime dataset.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2019.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 11; 2024: 11; 2025: 7; 2026: 2.

48 papers.

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  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  2. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  3. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  4. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 88
  5. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  6. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  7. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  8. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  9. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  10. 2024Hawk: Learning to Understand Open-World Video Anomaliescited by 47
  11. 2024Video Anomaly Detection and Explanation via Large Language Modelscited by 45
  12. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
36 more
  1. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  2. 2021Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Videocited by 32
  3. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  4. 2022DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Networkcited by 26
  5. 2023TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detectioncited by 23
  6. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  7. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15
  8. 2023Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Spacecited by 12
  9. 2022Consistency-based Self-supervised Learning for Temporal Anomaly Localizationcited by 10
  10. 2022Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detectioncited by 8
  11. 2021Weakly-supervised Joint Anomaly Detection and Classificationcited by 7
  12. 2022Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videoscited by 7
  13. 2024Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Cameracited by 7
  14. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  15. 2024Cross-Domain Learning for Video Anomaly Detection with Limited Supervisioncited by 6
  16. 2025Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detectioncited by 6
  17. 2024Are you Struggling? Dataset and Baselines for Struggle Determination in Assembly Videoscited by 5
  18. 2023Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detectioncited by 4
  19. 2024Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Surveycited by 4
  20. 2025Autoregressive Denoising Score Matching is a Good Video Anomaly Detectorcited by 4
  21. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  22. 2021Global Information Guided Video Anomaly Detectioncited by 2
  23. 2024Multiple Instance Learning for Cheating Detection and Localization in Online Examinationscited by 2
  24. 2024Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detectioncited by 2
  25. 2025The Role of Video Generation in Enhancing Data-Limited Action Understandingcited by 2
  26. 2025FrameShield: Adversarially Robust Video Anomaly Detectioncited by 2
  27. 2022Look at Adjacent Frames: Video Anomaly Detection without Offline Trainingcited by 1
  28. 2024TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillancecited by 1
  29. 2025Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detectioncited by 1
  30. 2021Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networkscited by 0
  31. 2022Real-world Video Anomaly Detection by Extracting Salient Features in Videoscited by 0
  32. 2023The Staged Knowledge Distillation in Video Classification: Harmonizing Student Progress by a Complementary Weakly Supervised Frameworkcited by 0
  33. 2023Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detectioncited by 0
  34. 2024Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformercited by 0
  35. 2026Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithmscited by 0
  36. 2026A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipationcited by 0