mlsanityData to 7 Oct 2026

A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods

arXiv 2111.01604 ↗First posted 2 Nov 2021cs.CVcited by 7

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Abstract (arXiv)

Video anomaly detection is one of the hot research topics in computer vision nowadays, as abnormal events contain a high amount of information. Anomalies are one of the main detection targets in surveillance systems, usually needing real-time actions. Regarding the availability of labeled data for training (i.e., there is not enough labeled data for abnormalities), semi-supervised anomaly detection approaches have gained interest recently. This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches, based on a common strategy they use for anomaly detection. Our goal is to help researchers develop more effective video anomaly detection methods. As the selection of a right Deep Neural Network plays an important role for several parts of this task, a quick comparative review on DNNs is prepared first. Unlike previous surveys, DNNs are reviewed from a spatiotemporal feature extraction viewpoint, customized for video anomaly detection. This part of the review can help researchers in this field select suitable networks for different parts of their methods. Moreover, some of the state-of-the-art anomaly detection methods, based on their detection strategy, are critically surveyed. The review provides a novel and deep look at existing methods and results in stating the shortcomings of these approaches, which can be a hint for future works.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 64 of its 163 references that are in the map. First posted on arXiv 2 Nov 2021.

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64 of 163 references.

References · 64

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  6. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  7. 2017A Closer Look at Spatiotemporal Convolutions for Action Recognitioncited by 1,139
  8. 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
  9. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  10. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  11. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  12. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
38 more
  1. 2015Unsupervised Learning of Video Representations using LSTMscited by 434
  2. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  3. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  4. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  5. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  6. 2016Adversarial Feature Learningcited by 350
  7. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  8. 2016Learning Temporal Regularity in Video Sequencescited by 254
  9. 2019A Survey of the Recent Architectures of Deep Convolutional Neural Networkscited by 211
  10. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  11. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
  12. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  13. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  14. 2016Learning Features by Watching Objects Movecited by 138
  15. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  16. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  17. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  18. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  19. 2018Adversarially Learned Anomaly Detectioncited by 115
  20. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  21. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 103
  22. 2019Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detectioncited by 100
  23. 2019Unsupervised Learning of Object Structure and Dynamics from Videoscited by 92
  24. 2016Spatiotemporal Residual Networks for Video Action Recognitioncited by 91
  25. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  26. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  27. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  28. 2016Medical image denoising using convolutional denoising autoencoderscited by 76
  29. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  30. 2017Abnormal Event Detection in Videos using Spatiotemporal Autoencodercited by 57
  31. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  32. 2017Unmasking the abnormal events in videocited by 48
  33. 2018An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videoscited by 45
  34. 2018Multi-Fiber Networks for Video Recognitioncited by 34
  35. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  36. 2019A Survey on GANs for Anomaly Detectioncited by 32
  37. 2019Future Frame Prediction Using Convolutional VRNN for Anomaly Detectioncited by 27
  38. 2019A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trendscited by 26

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 3.

7 papers.

  1. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  2. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  3. 2022Multi-Task Learning based Video Anomaly Detection with Attentioncited by 6
  4. 2025Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detectioncited by 4
  5. 2022Object Class Aware Video Anomaly Detection through Image Translationcited by 3
  6. 2023Future Video Prediction from a Single Frame for Video Anomaly Detectioncited by 2
  7. 2025HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detectioncited by 0