论文标题
边缘视频分析：有关应用，系统和启用技术的调查
Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques
论文作者
论文摘要
视频是全球数字信息爆炸的关键驱动力，可以为人类社会带来巨大的好处。政府和企业正在为各种应用程序部署无数摄像机，例如执法，应急管理，交通管制和安全监视，这都是由视频分析（VA）促进的。深度学习（DL）的快速发展刺激了这一趋势，该趋势可实现对象分类，检测和跟踪的更精确的模型。同时，随着互联网连接设备的扩散，每天生成大量数据，使云压倒了。 Edge Computing是一种新兴范式，将工作负载和服务从网络核心移至网络边缘，已被广泛认为是有前途的解决方案。由此产生的新十字路口Edge Video Analytics（EVA）开始引起广泛的关注。然而，在这个主题上只有几次与之松散有关的调查。由于该领域的快速发展，EVA的基本概念（例如，定义，体系结构）并未完全阐明。为了填补这些空白，我们对最近对EVA的努力进行了全面的调查。在本文中，我们首先回顾了边缘计算的基本原理，然后是VA的概述。接下来将讨论EVA系统及其支持技术。此外，我们介绍了普遍的框架和数据集，以帮助未来的研究人员进行EVA系统的开发。最后，我们讨论了现有的挑战，并预见了未来的研究方向。我们认为，这项调查将帮助读者理解VA和Edge计算之间的关系，并在EVA上引发新的想法。
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.
