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. The EVA system and its 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.

翻译：视频作为全球数字信息爆炸的关键驱动力，可为人类社会创造巨大效益。政府和企业正在部署海量摄像头以支持多种应用场景（例如：执法、应急管理、交通管控和安全监控），这些应用均依赖于视频分析技术。这一趋势得益于深度学习（DL）的快速发展，该技术能够实现更精准的目标分类、检测和追踪模型。与此同时，随着互联网连接设备的普及，每日产生的海量数据使云端不堪重负。边缘计算作为一种将工作负载与服务从网络核心迁移至网络边缘的新兴范式，已被广泛认可为极具前景的解决方案。由此催生的交叉领域——边缘视频分析（EVA）正引发广泛关注。然而，目前该领域仅有少量关联性较弱的综述文献。由于该领域的快速发展，EVA的基本概念（如定义、架构等）尚未得到充分阐释。为填补这些空白，本文对近年来EVA领域的研究成果进行了全面综述。首先回顾边缘计算基础理论，继而概述视频分析技术；随后讨论EVA系统及其使能技术；此外，介绍主流框架与数据集，为未来研究者开发EVA系统提供支撑；最后探讨现有挑战并展望未来研究方向。我们相信，本综述将帮助读者理解视频分析与边缘计算的关联性，并激发EVA领域的新思路。