In the past decade, it was a significant trend for surveillance applications to send huge amounts of real-time media data to the cloud via dedicated high-speed fiber networks. However, with the explosion of mobile devices and services in the era of Internet-of-Things, it becomes more promising to undertake real-time data processing at the edge of the network in a distributed way. Moreover, in order to reduce the investment of network deployment, media communication in surveillance applications is gradually changing to be wireless. It consequently poses great challenges to detect objects at the edge in a distributed and communication-efficient way. In this article, we propose an edge computing based object detection architecture to achieve distributed and efficient object detection via wireless communications for real-time surveillance applications. We first introduce the proposed architecture as well as its potential benefits, and identify the associated challenges in the implementation of the architecture. Then, a case study is presented to show our preliminary solution, followed by performance evaluation results. Finally, future research directions are pointed out for further studies.

中文翻译：

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边缘计算中的分布式高效对象检测：挑战与解决方案

在过去的十年中，监视应用程序通过专用的高速光纤网络将大量的实时媒体数据发送到云中已成为一个重要趋势。但是，随着物联网时代移动设备和服务的爆炸式增长，在网络边缘以分布式方式进行实时数据处理变得越来越有希望。而且，为了减少网络部署的投资，监视应用中的媒体通信正逐渐变为无线。因此，以分布式和高效通信的方式检测边缘物体会带来很大的挑战。在本文中，我们提出了一种基于边缘计算的对象检测架构，以通过无线通信实现实时监控应用中的分布式高效对象检测。我们首先介绍拟议的体系结构及其潜在的好处，并确定在体系结构实施中的相关挑战。然后，进行案例研究以展示我们的初步解决方案，然后给出性能评估结果。最后，指出了今后的研究方向，以进行进一步的研究。