mlsanityData to 7 Oct 2026

Context-aware Video Anomaly Detection in Long-Term Datasets

arXiv 2404.07887 ↗First posted 11 Apr 2024cs.CVcited by 11

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Abstract (arXiv)

Video anomaly detection research is generally evaluated on short, isolated benchmark videos only a few minutes long. However, in real-world environments, security cameras observe the same scene for months or years at a time, and the notion of anomalous behavior critically depends on context, such as the time of day, day of week, or schedule of events. Here, we propose a context-aware video anomaly detection algorithm, Trinity, specifically targeted to these scenarios. Trinity is especially well-suited to crowded scenes in which individuals cannot be easily tracked, and anomalies are due to speed, direction, or absence of group motion. Trinity is a contrastive learning framework that aims to learn alignments between context, appearance, and motion, and uses alignment quality to classify videos as normal or anomalous. We evaluate our algorithm on both conventional benchmarks and a public webcam-based dataset we collected that spans more than three months of activity.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 11 Apr 2024.

Builds on

24 of 40 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  11. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  12. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
12 more
  1. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  2. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  3. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  4. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  5. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  6. 2023Video Event Restoration Based on Keyframes for Video Anomaly Detectioncited by 54
  7. 2023Diversity-Measurable Anomaly Detectioncited by 41
  8. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  9. 2020Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Featurescited by 23
  10. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  11. 2016Robust Contextual Outlier Detection: Where Context Meets Sparsitycited by 11
  12. 2023Explainable Contextual Anomaly Detection using Quantile Regression Forestscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 4.

11 papers.

  1. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  2. 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmarkcited by 6
  3. 2024Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attentioncited by 5
  4. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  5. 2025Detecting Contextual Anomalies by Discovering Consistent Spatial Regionscited by 3
  6. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  7. 2026From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Securitycited by 3
  8. 2025Context-Aware Zero-Shot Anomaly Detection in Surveillance Using Contrastive and Predictive Spatiotemporal Modelingcited by 1
  9. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  10. 2026Context-structured Video Anomaly Detection with Large Vision-Language Modelscited by 0
  11. 2026ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understandingcited by 0