mlsanityData to 7 Oct 2026

Real-world Anomaly Detection in Surveillance Videos

arXiv 1801.04264 ↗First posted 12 Jan 2018cs.CVcited by 570

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Cited by 38 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is available at: https://webpages.uncc.edu/cchen62/dataset.html

Key facts. Cited by 570 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 74; 2022: 77; 2023: 78; 2024: 101; 2025: 117; 2026: 123.

570 papers.

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  1. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  2. 2022Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activitiescited by 284
  3. 2024MLVU: Benchmarking Multi-task Long Video Understandingcited by 275
  4. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  5. 2023A Survey on Video Diffusion Modelscited by 203
  6. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  7. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  8. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  9. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 121
  10. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  11. 2024VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understandingcited by 113
  12. 2023Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Modelscited by 107
38 more
  1. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  2. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  3. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
  4. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  5. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 88
  6. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  7. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 85
  8. 2021Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenescited by 83
  9. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  10. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  11. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  12. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  13. 2024Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularitycited by 66
  14. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  15. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 63
  16. 2021Unpaired Deep Image Deraining Using Dual Contrastive Learningcited by 60
  17. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  18. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  19. 2023Open-Vocabulary Video Anomaly Detectioncited by 57
  20. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  21. 2021Unsupervised Action Segmentation by Joint Representation Learning and Online Clusteringcited by 48
  22. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  23. 2021Learning by Aligning Videos in Timecited by 47
  24. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  25. 2024Hawk: Learning to Understand Open-World Video Anomaliescited by 47
  26. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  27. 2024Video Anomaly Detection and Explanation via Large Language Modelscited by 45
  28. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  29. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  30. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  31. 2023Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 42
  32. 2023Towards Surveillance Video-and-Language Understanding: New Dataset, Baselines, and Challengescited by 42
  33. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
  34. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  35. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  36. 2024SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanationscited by 37
  37. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  38. 2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detectioncited by 35

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