Data to 14 Oct 2026

Abnormal Event Detection in Videos using Generative Adversarial Nets

arXiv 1708.09644 ↗First posted 31 Aug 2017cs.CV cs.MMcited by 82

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Abstract (arXiv)

In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.

Key facts. Cited by 82 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Aug 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 28; 2022: 26; 2023: 11; 2024: 7; 2025: 7; 2026: 3.

82 papers.

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2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 93
2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31

38 more

2021Learning Not to Reconstruct Anomaliescited by 31
2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
2023Exploring Diffusion Models for Unsupervised Video Anomaly Detectioncited by 23
2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
2022EVAL: Explainable Video Anomaly Localizationcited by 19
2023Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representationscited by 19
2021FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformationcited by 15
2021Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Reviewcited by 15
2023Holistic Representation Learning for Multitask Trajectory Anomaly Detectioncited by 15
2021OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detectioncited by 14
2022Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomaliescited by 14
2021Discrete neural representations for explainable anomaly detectioncited by 12
2022Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videoscited by 12
2022Anomaly Detection in Aerial Videos with Transformerscited by 11
2021Image/Video Deep Anomaly Detection: A Surveycited by 10
2021Anomaly Detection in Video Sequences: A Benchmark and Computational Modelcited by 10
2021X-MAN: Explaining multiple sources of anomalies in videocited by 10
2023Making Reconstruction-based Method Great Again for Video Anomaly Detectioncited by 10
2023Survey on video anomaly detection in dynamic scenes with moving camerascited by 10
2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
2024STNMamba: Mamba-based Spatial-Temporal Normality Learning for Video Anomaly Detectioncited by 9
2022Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detectioncited by 8
2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
2021A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methodscited by 7
2022BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detectioncited by 7
2021A Modular and Unified Framework for Detecting and Localizing Video Anomaliescited by 6
2025SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Modelcited by 6
2022Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Modelcited by 5
2022Exploiting Spatial-temporal Correlations for Video Anomaly Detectioncited by 5
2022LGN-Net: Local-Global Normality Network for Video Anomaly Detectioncited by 5
2021Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomaliescited by 4
2024Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesiscited by 4
2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4

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