The increasing prevalence 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. This article elucidates core concepts by reviewing recent advances and typical solutions, and aggregating available research resources (e.g., literatures, code, tools, and workshops) accessible at https://github.com/fdjingliu/NSVAD. Additionally, we showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD to further elucidate its potential scope of research and application. 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愈发受到关注，这推动了自动视频异常检测（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在智慧城市和视频互联网等新兴应用场景中取得了显著进展，其研究范围已超越传统的算法工程，扩展到可部署的VAD网络系统（NSVAD），这成为AI、物联网视频（IoVT）和计算领域交叉探索的实践热点。本文阐述了多种深度学习驱动的VAD路线的基本假设、学习框架和适用场景，为NSVAD的初学者提供了详尽的教程。通过回顾近期进展和典型解决方案，并汇总可获取的研究资源（例如文献、代码、工具和工作坊，地址为https://github.com/fdjingliu/NSVAD），本文阐明了核心概念。此外，我们展示了在工业物联网和智慧城市领域的最新NSVAD研究，以及一种用于可部署NSVAD的端云协同架构，以进一步揭示其潜在的研究与应用范围。最后，本文展望了未来发展趋势，并讨论了AI与计算技术融合如何应对现有研究挑战、促进开放机遇，从而为未来的研究人员和工程师提供富有见地的指导。