  Video, as a key driver in the global explosion of digital information, can
create tremendous benefits for human society. Governments and enterprises are
deploying innumerable cameras for a variety of applications, e.g., law
enforcement, emergency management, traffic control, and security surveillance,
all facilitated by video analytics (VA). This trend is spurred by the rapid
advancement of deep learning (DL), which enables more precise models for object
classification, detection, and tracking. Meanwhile, with the proliferation of
Internet-connected devices, massive amounts of data are generated daily,
overwhelming the cloud. Edge computing, an emerging paradigm that moves
workloads and services from the network core to the network edge, has been
widely recognized as a promising solution. The resulting new intersection, edge
video analytics (EVA), begins to attract widespread attention. Nevertheless,
only a few loosely-related surveys exist on this topic. The basic concepts of
EVA (e.g., definition, architectures) were not fully elucidated due to the
rapid development of this domain. To fill these gaps, we provide a
comprehensive survey of the recent efforts on EVA. In this paper, we first
review the fundamentals of edge computing, followed by an overview of VA. EVA
systems and their enabling techniques are discussed next. In addition, we
introduce prevalent frameworks and datasets to aid future researchers in the
development of EVA systems. Finally, we discuss existing challenges and foresee
future research directions. We believe this survey will help readers comprehend
the relationship between VA and edge computing, and spark new ideas on EVA.
