mlsanityData to 7 Oct 2026

Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes

arXiv 1609.00866 ↗First posted 3 Sep 2016cs.CVcited by 50

Not on the map. Why?

Abstract (arXiv)

The detection of abnormal behaviours in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks (FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that detection and localization of the proposed method outperforms existing methods in terms of accuracy.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 17; 2022: 12; 2023: 5; 2024: 7; 2025: 6; 2026: 2.

50 papers.

Sort:

By area:

  1. 2021Student-Teacher Feature Pyramid Matching for Anomaly Detectioncited by 145
  2. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  3. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  4. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  5. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  6. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  7. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  8. 2021Deep Learning-based Action Detection in Untrimmed Videos: A Surveycited by 32
  9. 2022AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decodercited by 29
  10. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  11. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  12. 2021DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detectioncited by 21
38 more
  1. 2020Sequential Anomaly Detection using Inverse Reinforcement Learningcited by 19
  2. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
  3. 2021Image/Video Deep Anomaly Detection: A Surveycited by 10
  4. 2021Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representationscited by 10
  5. 2022Boosting Out-of-Distribution Detection with Multiple Pre-trained Modelscited by 7
  6. 2023A Prototype-Based Neural Network for Image Anomaly Detection and Localizationcited by 7
  7. 2021Anomaly Detection using Edge Computing in Video Surveillance System: Reviewcited by 6
  8. 2024Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detectioncited by 6
  9. 2021Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Frameworkcited by 5
  10. 2022Out of Distribution Detection on ImageNet-Ocited by 5
  11. 2024Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Surveycited by 5
  12. 2024A Lightweight Measure of Classification Difficulty from Application Dataset Characteristicscited by 4
  13. 2024Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networkscited by 4
  14. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  15. 2022Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detectioncited by 3
  16. 2022Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillationcited by 3
  17. 2023Data-Side Efficiencies for Lightweight Convolutional Neural Networkscited by 3
  18. 2023Human-centric Behavior Description in Videos: New Benchmark and Modelcited by 3
  19. 2021SLA2P: Self-supervised Anomaly Detection with Adversarial Perturbationcited by 2
  20. 2021Multimedia Datasets for Anomaly Detection: A Reviewcited by 2
  21. 2024Detection of Object Throwing Behavior in Surveillance Videoscited by 2
  22. 2025ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluationcited by 2
  23. 2021Evaluating the Use of Reconstruction Error for Novelty Localizationcited by 1
  24. 2021Video Abnormal Event Detection by Learning to Complete Visual Cloze Testscited by 1
  25. 2022Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videoscited by 1
  26. 2023A Novel Voronoi-based Convolutional Neural Network Framework for Pushing Person Detection in Crowd Videoscited by 1
  27. 2025SLICK: Selective Localization and Instance Calibration for Knowledge-Enhanced Car Damage Segmentation in Automotive Insurancecited by 1
  28. 2025Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Frameworkcited by 1
  29. 2021Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networkscited by 0
  30. 2021Generalized One-Class Learning Using Pairs of Complementary Classifierscited by 0
  31. 2021Abnormal Behavior Detection Based on Target Analysiscited by 0
  32. 2021Generatively Augmented Neural Network Watchdog for Image Classification Networkscited by 0
  33. 2021Anomaly Detection using Capsule Networks for High-dimensional Datasetscited by 0
  34. 2022Industrial Scene Change Detection using Deep Convolutional Neural Networkscited by 0
  35. 2025Anomaly Detection via Autoencoder Composite Features and NCEcited by 0
  36. 2025Double Helix Diffusion for Cross-Domain Anomaly Image Generationcited by 0
  37. 2026TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detectioncited by 0
  38. 2026Efficient Neural Network Model Selection for Few-Class Application Datasetscited by 0