mlsanityData to 14 Oct 2026

Video Anomaly Detection in 10 Years: A Survey and Outlook

arXiv 2405.19387 ↗First posted 29 May 2024cs.CVcited by 18

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Abstract (arXiv)

Video anomaly detection (VAD) holds immense importance across diverse domains such as surveillance, healthcare, and environmental monitoring. While numerous surveys focus on conventional VAD methods, they often lack depth in exploring specific approaches and emerging trends. This survey explores deep learning-based VAD, expanding beyond traditional supervised training paradigms to encompass emerging weakly supervised, self-supervised, and unsupervised approaches. A prominent feature of this review is the investigation of core challenges within the VAD paradigms including large-scale datasets, features extraction, learning methods, loss functions, regularization, and anomaly score prediction. Moreover, this review also investigates the vision language models (VLMs) as potent feature extractors for VAD. VLMs integrate visual data with textual descriptions or spoken language from videos, enabling a nuanced understanding of scenes crucial for anomaly detection. By addressing these challenges and proposing future research directions, this review aims to foster the development of robust and efficient VAD systems leveraging the capabilities of VLMs for enhanced anomaly detection in complex real-world scenarios. This comprehensive analysis seeks to bridge existing knowledge gaps, provide researchers with valuable insights, and contribute to shaping the future of VAD research.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 71 of its 132 references that are in the map. First posted on arXiv 29 May 2024.

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71 of 132 references.

References · 71

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38 more
  1. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  2. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  3. 2022Expanding Language-Image Pretrained Models for General Video Recognitioncited by 321
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  8. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 7; 2026: 8.

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  1. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  2. 2024Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformercited by 6
  3. 2026Failure Identification in Imitation Learning Via Statistical and Semantic Filteringcited by 6
  4. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  5. 2025Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challengescited by 3
  6. 2026MemoVAD: Resource-Efficient Video Anomaly Detection via Dynamic Semantic Memory in Edge Computing Scenarioscited by 2
  7. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  8. 2024Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performancecited by 0
  9. 2025Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plantscited by 0
  10. 2025Vision-Based Mistake Analysis in Procedural Activities: A Review of Advances and Challengescited by 0
  11. 2025Short-Window Sliding Learning for Real-Time Violence Detection via LLM-based Auto-Labelingcited by 0
  12. 2025Deep Learning Perspective of Scene Understanding in Autonomous Robotscited by 0
6 more
  1. 2025TorchTraceAP: A New Benchmark Dataset for Detecting Performance Anti-Patterns in Computer Vision Modelscited by 0
  2. 2026Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inferencecited by 0
  3. 2026Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmarkcited by 0
  4. 2026LADBench: A Benchmark for Logical Fault Detection in Imagescited by 0
  5. 2026FedVAR: Prototype-Aligned Federated Framework for Video Anomaly Recognitioncited by 0
  6. 2026Copy What Is Seen, Generate What Is Not: Training-Free Anomaly-Aware Video Restorationcited by 0

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  1. 2024Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight
  2. 2024Harnessing Large Language Models for Training-free Video Anomaly Detection
  3. 2025SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
  4. 2025From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
  5. 2025MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detection
  6. 2026CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection
  7. 2026Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models
  8. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detection