  Edge computing has been getting a momentum with ever-increasing data at the
edge of the network. In particular, huge amounts of video data and their
real-time processing requirements have been increasingly hindering the
traditional cloud computing approach due to high bandwidth consumption and high
latency. Edge computing in essence aims to overcome this hindrance by
processing most video data making use of edge servers, such as small-scale
on-premises server clusters, server-grade computing resources at mobile base
stations and even mobile devices like smartphones and tablets; hence, the term
edge-based video analytics. However, the actual realization of such analytics
requires more than the simple, collective use of edge servers. In this paper,
we survey state-of-the-art works on edge-based video analytics with respect to
applications, architectures, techniques, resource management, security and
privacy. We provide a comprehensive and detailed review on what works, what
doesn't work and why. These findings give insights and suggestions for next
generation edge-based video analytics. We also identify open issues and
research directions.
