The increasing utilization of surveillance cameras in smart cities, coupled with the surge of online video applications, has heightened concerns regarding public security and privacy protection, which propelled automated Video Anomaly Detection (VAD) into a fundamental research task within the Artificial Intelligence (AI) community. With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes, offering an exhaustive tutorial for novices in NSVAD. In addition, this article elucidates core concepts by reviewing recent advances and typical solutions and aggregating available research resources accessible at https://github.com/fdjingliu/NSVAD. Lastly, this article projects future development trends and discusses how the integration of AI and computing technologies can address existing research challenges and promote open opportunities, serving as an insightful guide for prospective researchers and engineers.

翻译：随着智慧城市中监控摄像头的日益普及以及在线视频应用的激增，公共安全与隐私保护问题备受关注，这推动了自动化视频异常检测（VAD）成为人工智能（AI）领域的一项基础研究任务。随着深度学习和边缘计算的发展，VAD 已取得显著进展，并与智慧城市和视频互联网中的新兴应用协同发展，其研究范畴已超越传统的算法工程，迈向可部署的面向 VAD 的网络化系统（NSVAD），成为 AI、视频物联网（IoVT）与计算领域交叉探索的一个实践热点。本文系统阐述了各类深度学习驱动的 VAD 技术路径的基本假设、学习框架及适用场景，为 NSVAD 领域的新手提供一份详尽的入门教程。此外，本文通过综述近期进展与典型解决方案，并汇总发布于 https://github.com/fdjingliu/NSVAD 的可获取研究资源，以阐明核心概念。最后，本文展望了未来发展趋势，并探讨了 AI 与计算技术的融合如何应对现有研究挑战并催生新的开放机遇，旨在为未来的研究者与工程师提供一份具有深刻见解的指南。