Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiable information. The lack of transparency and interpretability in video transmission and usage raises public concerns about privacy and ethics, limiting the real-world application of VAD. Recently, researchers have focused on privacy concerns in VAD by conducting systematic studies from various perspectives including data, features, and systems, making Privacy-Preserving Video Anomaly Detection (P2VAD) a hotspot in the AI community. However, current research in P2VAD is fragmented, and prior reviews have mostly focused on methods using RGB sequences, overlooking privacy leakage and appearance bias considerations. To address this gap, this article systematically reviews the progress of P2VAD for the first time, defining its scope and providing an intuitive taxonomy. We outline the basic assumptions, learning frameworks, and optimization objectives of various approaches, analyzing their strengths, weaknesses, and potential correlations. Additionally, we provide open access to research resources such as benchmark datasets and available code. Finally, we discuss key challenges and future opportunities from the perspectives of AI development and P2VAD deployment, aiming to guide future work in the field.

翻译：视频异常检测旨在自动分析开放空间采集的监控视频中的时空模式，以检测可能造成非接触性伤害的异常事件。然而，基于视觉的监控系统（如闭路电视）常会捕获个人可识别信息。视频传输与使用过程中透明度和可解释性的缺失引发了公众对隐私与伦理的担忧，限制了视频异常检测的实际应用。近年来，研究者从数据、特征、系统等多重视角对视频异常检测中的隐私问题展开系统性研究，使得隐私保护视频异常检测成为人工智能领域的热点。然而，当前该领域研究较为零散，既往综述多聚焦于基于RGB序列的方法，忽视了隐私泄露与外观偏差的考量。为填补这一空白，本文首次系统综述隐私保护视频异常检测的研究进展，界定其范畴并提供直观的分类体系。我们梳理了各类方法的基本假设、学习框架与优化目标，分析其优势、局限及潜在关联。此外，我们公开了基准数据集与可用代码等研究资源。最后，我们从人工智能发展与隐私保护视频异常检测部署的视角探讨了关键挑战与未来机遇，以期为该领域的后续研究提供指引。