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

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques