  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.
