综述深度学习在人、车、环境三类视频异常检测中的方法与挑战
A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
- 按监督级别和自适应学习方式系统梳理现有方法
- 覆盖人、车、环境三类场景，分析各自难点与设计思路
- 适合想快速了解领域全貌的研究者和应用开发者
视频异常检测（VAD）已成为计算机视觉中的关键任务，在多个领域具有广泛意义。近年来深度学习的进展推动了该领域的显著进步，但研究仍分散于不同领域和学习范式中。本文系统综述了VAD研究，按监督层级及自适应学习方法（如在线、主动、持续学习）进行组织。我们分析了三大应用场景：以人为核心、以车为核心和以环境为核心，每类均面临独特挑战与设计考量。通过整合子领域洞察，识别当前方法的根本贡献与局限性。本综述旨在为社区提供理论与应用并重的结构化基础，支持研究推进，并揭示包括基础研究问题与实际部署障碍在内的开放挑战。
原文摘要 · Abstract (English)
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.
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