mlsanityData to 14 Oct 2026

A Modular and Unified Framework for Detecting and Localizing Video Anomalies

arXiv 2103.11299 ↗First posted 21 Mar 2021cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos. Extensive performance evaluations on benchmark datasets show that the proposed framework significantly outperforms the current state-of-the-art approaches.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 33 of its 50 references that are in the map. First posted on arXiv 21 Mar 2021.

Builds on

33 of 50 references.

References · 33

  1. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2016Matching Networks for One Shot Learningcited by 2,255
  5. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  6. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 784
  7. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  8. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  9. 2016RMPE: Regional Multi-person Pose Estimationcited by 340
  10. 2017CORe50: a New Dataset and Benchmark for Continuous Object Recognitioncited by 289
  11. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  12. 2016Learning Temporal Regularity in Video Sequencescited by 254
21 more
  1. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  2. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  3. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 111
  4. 2019Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videoscited by 104
  5. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  6. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 90
  7. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  8. 2020Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detectioncited by 83
  9. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  10. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  11. 2020Few-shot Scene-adaptive Anomaly Detectioncited by 62
  12. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
  13. 2017Abnormal Event Detection in Videos using Spatiotemporal Autoencodercited by 57
  14. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  15. 2020Continual Learning for Anomaly Detection in Surveillance Videoscited by 49
  16. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  17. 2016Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detectioncited by 32
  18. 2017Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowdscited by 28
  19. 2016A Discriminative Framework for Anomaly Detection in Large Videoscited by 25
  20. 2019Anomaly Locality in Video Surveillancecited by 18
  21. 2019A Delay Metric for Video Object Detection: What Average Precision Fails to Tellcited by 9

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 0; 2025: 2.

6 papers.

  1. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  2. 2021Learnable Locality-Sensitive Hashing for Video Anomaly Detectioncited by 17
  3. 2022Adversarial Machine Learning Attacks Against Video Anomaly Detection Systemscited by 9
  4. 2025ComplexVAD: Detecting Interaction Anomalies in Videocited by 8
  5. 2022Look at Adjacent Frames: Video Anomaly Detection without Offline Trainingcited by 1
  6. 2025Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approachescited by 0