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.

自动化监控对于保护未来智能城市中的关键基础设施（CI）至关重要。动态环境和带宽需求要求系统在发生感兴趣的事件时能够做出反应。我们提供了一种可重新配置的网络物理系统，用于使用分布式云边缘智能视频监控来保护CI。我们的本地边缘节点通过深度学习执行人员检测。处理功能嵌入高性能SoC（片上系统）中，从而实现实时性能（$\approx$100 fps-每秒帧数），从而可以以较低的帧频高效管理更多摄像机源的视频流。云服务器从节点收集结果以执行生物特征识别，跟踪和周边监视。质量和资源管理模块监视数据带宽并触发重新配置以适应传输的视频分辨率。这还使多个摄像机可以灵活使用网络，同时保持生物识别的准确性。一个真实的例子表明减少了$\approx$ 相对于无重配置方案，使用了75％的带宽。