mlsanityData to 7 Oct 2026

EVAL: Explainable Video Anomaly Localization

arXiv 2212.07900 ↗First posted 15 Dec 2022cs.CVcited by 19

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Abstract (arXiv)

We develop a novel framework for single-scene video anomaly localization that allows for human-understandable reasons for the decisions the system makes. We first learn general representations of objects and their motions (using deep networks) and then use these representations to build a high-level, location-dependent model of any particular scene. This model can be used to detect anomalies in new videos of the same scene. Importantly, our approach is explainable - our high-level appearance and motion features can provide human-understandable reasons for why any part of a video is classified as normal or anomalous. We conduct experiments on standard video anomaly detection datasets (Street Scene, CUHK Avenue, ShanghaiTech and UCSD Ped1, Ped2) and show significant improvements over the previous state-of-the-art.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

23 of 51 references.

References · 23

  1. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  2. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  3. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  4. 2016Learning Temporal Regularity in Video Sequencescited by 254
  5. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 147
  6. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  7. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  8. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  9. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  10. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  11. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  12. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 85
11 more
  1. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  2. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  3. 2020Few-shot Scene-adaptive Anomaly Detectioncited by 62
  4. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  5. 2020Continual Learning for Anomaly Detection in Surveillance Videoscited by 49
  6. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  7. 2016Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detectioncited by 32
  8. 2020Learning a distance function with a Siamese network to localize anomalies in videoscited by 31
  9. 2018Detecting abnormal events in video using Narrowed Normality Clusterscited by 30
  10. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  11. 2021An Efficient Approach for Anomaly Detection in Traffic Videoscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 5; 2026: 3.

19 papers.

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  1. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  2. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  3. 2024Context-aware Video Anomaly Detection in Long-Term Datasetscited by 11
  4. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  5. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  6. 2025ComplexVAD: Detecting Interaction Anomalies in Videocited by 7
  7. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  8. 2024Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detectioncited by 6
  9. 2025Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detectioncited by 4
  10. 2025Detecting Contextual Anomalies by Discovering Consistent Spatial Regionscited by 3
  11. 2026Bounding-Box Trajectories Matter for Video Anomaly Detectioncited by 3
  12. 2023Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approachcited by 1
7 more
  1. 2024An Integrated Framework for Multi-Granular Explanation of Video Summarizationcited by 1
  2. 2024Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplifiedcited by 1
  3. 2024Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detectioncited by 0
  4. 2025Video Anomaly Detection with Contours -- A Studycited by 0
  5. 2025An Experimental Study on Generating Plausible Textual Explanations for Video Summarizationcited by 0
  6. 2026Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inferencecited by 0
  7. 2026GuidedFlow: An Attention-Guided Framework for Anomaly Detection in Additive Manufacturingcited by 0