论文标题
通过智能视频监视，可重新配置的网络物理系统，用于智能城市的关键基础设施保护
Reconfigurable Cyber-Physical System for Critical Infrastructure Protection in Smart Cities via Smart Video-Surveillance
论文作者
论文摘要
自动监视对于保护未来智能城市的关键基础设施（CI）至关重要。动态环境和带宽需求需求系统适应自身在发生感兴趣事件时做出反应。我们提出了可重新配置的网络物理系统，可使用分布式云边缘智能视频监视保护CI。我们的本地边缘节点通过深度学习来执行人们的检测。处理嵌入了高性能SOC（芯片系统中）中，可实现实时性能（$ \ $ \ $ \ $ 100 fps-帧每秒），这可以有效地以较低的帧速率管理更多相机源的视频流。云服务器收集的结果是从节点进行生物识别面部识别，跟踪和周边监测。质量和资源管理模块监视数据带宽和触发器的重新配置，以适应传输的视频分辨率。这也使通过多个相机可以灵活地使用网络，同时保持生物识别的准确性。一个现实世界中的示例显示了$ \ $ \ $ \ $ 75 \％带宽相对于无重新配置方案的使用。
Automated surveillance is essential for the protection of Critical Infrastructures (CIs) in future Smart Cities. The dynamic environments and bandwidth requirements demand systems that adapt themselves to react when events of interest occur. We present a reconfigurable Cyber Physical System for the protection of CIs using distributed cloud-edge smart video surveillance. Our local edge nodes perform people detection via Deep Learning. Processing is embedded in high performance SoCs (System-on-Chip) achieving real-time performance ($\approx$ 100 fps - frames per second) which enables efficiently managing video streams of more cameras source at lower frame rate. Cloud server gathers results from nodes to carry out biometric facial identification, tracking, and perimeter monitoring. A Quality and Resource Management module monitors data bandwidth and triggers reconfiguration adapting the transmitted video resolution. This also enables a flexible use of the network by multiple cameras while maintaining the accuracy of biometric identification. A real-world example shows a reduction of $\approx$ 75\% bandwidth use with respect to the no-reconfiguration scenario.
