mlsanityData to 14 Oct 2026

An Attribute-based Method for Video Anomaly Detection

arXiv 2212.00789 ↗First posted 1 Dec 2022cs.CV cs.LGcited by 21

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Abstract (arXiv)

Video anomaly detection (VAD) identifies suspicious events in videos, which is critical for crime prevention and homeland security. In this paper, we propose a simple but highly effective VAD method that relies on attribute-based representations. The base version of our method represents every object by its velocity and pose, and computes anomaly scores by density estimation. Surprisingly, this simple representation is sufficient to achieve state-of-the-art performance in ShanghaiTech, the most commonly used VAD dataset. Combining our attribute-based representations with an off-the-shelf, pretrained deep representation yields state-of-the-art performance with a 99.1%, 93.7%, and 85.9% AUROC on Ped2, Avenue, and ShanghaiTech, respectively.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2022.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 7; 2025: 3; 2026: 5.

21 papers.

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  1. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 49
  2. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  3. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 22
  4. 2024An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Predictioncited by 19
  5. 2025AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMcited by 15
  6. 2025A Unified Reasoning Framework for Holistic Zero-Shot Video Anomaly Analysiscited by 12
  7. 2023Survey on video anomaly detection in dynamic scenes with moving camerascited by 10
  8. 2023No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detectioncited by 7
  9. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  10. 2023Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challengecited by 1
  11. 2023Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approachcited by 1
  12. 2023Beyond the Benchmark: Detecting Diverse Anomalies in Videoscited by 1
9 more
  1. 2024uTRAND: Unsupervised Anomaly Detection in Traffic Trajectoriescited by 1
  2. 2026Real-Time Video Anomaly Detection Using YOLO Pose Estimation and CLIP-Based Semantic Scoringcited by 1
  3. 2026Probe-VAD: Ordinal Likelihood Probing for Training-Free Video Anomaly Detectioncited by 1
  4. 2023Reasonable Anomaly Detection in Long Sequencescited by 0
  5. 2024Patch Spatio-Temporal Relation Prediction for Video Anomaly Detectioncited by 0
  6. 2025VelocityNet: Real-Time Crowd Anomaly Detection via Person-Specific Velocity Analysiscited by 0
  7. 2026GridVAD: Open-Set Video Anomaly Detection via Spatial Reasoning over Stratified Frame Gridscited by 0
  8. 2026Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluatedcited by 0
  9. 2026A Unified Score Matching Paradigm for Video Anomaly Detection and Anticipationcited by 0