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：挑战和解决方案


在过去的十年中，通过专用高速光纤网络将大量实时媒体数据发送到云端是监控应用的一个重要趋势。然而，随着物联网时代移动设备和服务的爆炸式增长，以分布式方式在网络边缘进行实时数据处理变得更有前景。此外，为了减少网络部署的投资，监控应用中的媒体通信正在逐渐向无线化转变。因此，以分布式且高效通信的方式检测边缘物体提出了巨大的挑战。在本文中，我们提出了一种基于边缘计算的对象检测架构，通过无线通信实现分布式、高效的对象检测，用于实时监控应用。我们首先介绍所提出的架构及其潜在好处，并确定该架构实施中的相关挑战。然后，通过案例研究来展示我们的初步解决方案，然后是性能评估结果。最后指出了今后进一步研究的方向。