Edge computing has been getting a momentum with ever-increasing data at the edge of the network. In particular, huge amounts of video data and their real-time processing requirements have been increasingly hindering the traditional cloud computing approach due to high bandwidth consumption and high latency. Edge computing in essence aims to overcome this hindrance by processing most video data making use of edge servers, such as small-scale on-premises server clusters, server-grade computing resources at mobile base stations and even mobile devices like smartphones and tablets; hence, the term edge-based video analytics. However, the actual realization of such analytics requires more than the simple, collective use of edge servers. In this paper, we survey state-of-the-art works on edge-based video analytics with respect to applications, architectures, techniques, resource management, security and privacy. We provide a comprehensive and detailed review on what works, what doesn't work and why. These findings give insights and suggestions for next generation edge-based video analytics. We also identify open issues and research directions.

翻译：边缘计算正随着网络边缘数据的持续增长而获得发展动力。特别是海量视频数据及其实时处理需求，由于高带宽消耗和高延迟，日益阻碍了传统的云计算方法。边缘计算本质上旨在通过利用边缘服务器（例如小型本地服务器集群、移动基站上的服务器级计算资源，甚至智能手机和平板电脑等移动设备）处理大部分视频数据来克服这一障碍；因此，产生了“基于边缘的视频分析”这一术语。然而，此类分析的实际实现不仅仅需要简单、集体地使用边缘服务器。本文综述了关于基于边缘的视频分析的最新研究工作，涵盖应用、架构、技术、资源管理、安全与隐私等方面。我们全面且详细地评述了哪些方法有效、哪些无效及其原因。这些发现为下一代基于边缘的视频分析提供了见解和建议。我们还指出了未解决的问题及研究方向。