mlsanityData to 14 Oct 2026

A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment

arXiv 2508.14203 ↗First posted 19 Aug 2025cs.CV cs.AIcited by 4

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Abstract (arXiv)

Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significant progress in this area, yet the field remains fragmented across domains and learning paradigms. This survey offers a comprehensive perspective on VAD, systematically organizing the literature across various supervision levels, as well as adaptive learning methods such as online, active, and continual learning. We examine the state of VAD across three major application categories: human-centric, vehicle-centric, and environment-centric scenarios, each with distinct challenges and design considerations. In doing so, we identify fundamental contributions and limitations of current methodologies. By consolidating insights from subfields, we aim to provide the community with a structured foundation for advancing both theoretical understanding and real-world applicability of VAD systems. This survey aims to support researchers by providing a useful reference, while also drawing attention to the broader set of open challenges in anomaly detection, including both fundamental research questions and practical obstacles to real-world deployment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 75 of its 208 references that are in the map. First posted on arXiv 19 Aug 2025.

Builds on

75 of 208 references.

References · 75

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38 more
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  24. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  25. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
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50 most cited of 75 All 75 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentratingcited by 5
  2. 2026Steering and Rectifying Latent Representation Manifolds in Frozen Multi-modal LLMs for Video Anomaly Detectioncited by 4
  3. 2026Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wildcited by 2
  4. 2026Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaborationcited by 0