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. A dedicated venue for collecting and summarizing the latest advances of EVA is highly desired by the community. Besides, the basic concepts of EVA (e.g., definition, architectures, etc.) are ambiguous and neglected by these surveys due to the rapid development of this domain. A thorough clarification is needed to facilitate a consensus on these concepts. To fill in these gaps, we conduct 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:作为数字信息全球爆炸的关键驱动因素,视频作为数字信息全球爆炸的关键驱动力,可以对人类社会带来巨大的惠益。政府和企业正在为各种应用,例如执法、应急管理、交通控制和安全监控等,部署无数的相机,这些应用都得到了视频分析(VA)的推动。这一趋势受到深层次学习(DL)的快速发展(DL)的推动,这为物体分类、探测和跟踪提供了更精确的模式。与此同时,随着互联网连接设备的扩散,每天生成大量数据,云层增加。EVA计算,一种将工作量和服务从网络核心转移到网络边缘的新兴模式,被广泛承认为有希望的解决办法。由此产生的新的交叉点、边缘视频分析(EVA)开始引起广泛的关注。然而,关于这个主题,只有少数与广泛相关的调查(DLL),一个专门用来收集和总结EVA最新进展的场所是社区所非常希望的。此外,在EVA的基本概念(例如,我们定义、架构等等)中,由于EVA现有挑战的边缘,我们受到模糊和忽视,这些调查正在被这些调查的新的模式被这些调查的新的模式被这些系统被这些系统的快速理解, 使得EVA 能够理解。在EVA中,我们对这些域域域的研究中进行一个彻底的研究中,我们对这些研究中, 相信这些研究中,我们对这些研究的将来的将来的研究和将来的研究需要的系统进行一个完整的分析中,一个完整的分析中,我们对这些论文中进行一个完整的研究的系统进行一个完整的分析,我们对这些论文的观察到最后的观察。