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.

翻译：视频作为全球数字信息爆炸的关键驱动因素，可为人类社会带来巨大收益。政府和企业正在部署海量摄像头以支持各类应用（例如执法、应急管理、交通管控及安防监控），这些应用均依托视频分析技术实现。这一趋势得益于深度学习的快速发展，该技术使目标分类、检测与追踪的模型更加精确。与此同时，随着物联网设备的普及，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，已被广泛视为极具前景的解决方案。由此催生的边缘视频分析这一交叉领域开始受到广泛关注。然而，当前仅存在少量关联度较低的综述涉及该主题。由于该领域发展迅速，其基本概念（如定义、架构）尚未得到充分阐述。为填补这些空白，我们对边缘视频分析领域的最新成果进行了全面综述。本文首先回顾边缘计算的基本原理，随后概述视频分析技术。接着讨论边缘视频分析系统及其使能技术。此外，我们介绍主流框架与数据集以辅助未来研究者开发边缘视频分析系统。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系，并激发边缘视频分析领域的新思路。