Unified Physical Threat Monitoring System Aided by Virtual Building Simulation

Zenjie Li, B. Norton
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Abstract

With increasing physical threats in recent years targeted at critical infrastructures, it is crucial to establish a reliable threat monitoring system integrating video surveillance and digital sensors based on cutting-edge technologies. A physical threat monitoring solution unifying the floorplan, cameras, and sensors for smart buildings has been set up in our study. Computer vision and deep learning models are used for video streams analysis. When a threat is detected by a rule engine based on the real-time analysis results combining with feedback from related digital sensors, an alert is sent to the Video Management System so that human operators can take further action.A physical threat monitoring system typically needs to address complex and even destructive incidents, such as fire, which is unrealistic to simulate in real life. Restrictions imposed during the Covid-19 pandemic and privacy concerns have added to the challenges. Our study utilises the Unreal Engine to simulate some typical suspicious and intrusion scenes with photorealistic qualities in the context of a virtual building. Add-on programs are implemented to transfer the video stream from virtual PTZ cameras to the Milestone Video Management System and enable users to control those cameras from the graphic client application. Virtual sensors such as fire alarms, temperature sensors and door access controls are implemented similarly, fulfilling the same programmatic VMS interface as real-life sensors. Thanks to this simulation system’s extensibility and repeatability, we have consolidated this unified physical threat monitoring system and verified its effectiveness and user-friendliness. Both the simulated Unreal scenes and the software add-ons developed during this study are highly modulated and thereby are ready for reuse in future projects in this area.
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基于虚拟建筑仿真的统一物理威胁监控系统
近年来,针对关键基础设施的物理威胁越来越多,基于前沿技术,建立可靠的视频监控与数字传感器相结合的威胁监控系统至关重要。在我们的研究中,建立了一个统一智能建筑平面图,摄像头和传感器的物理威胁监控解决方案。计算机视觉和深度学习模型用于视频流分析。当规则引擎根据实时分析结果和相关数字传感器的反馈检测到威胁时,会向视频管理系统发送警报,以便操作人员采取进一步行动。物理威胁监测系统通常需要处理复杂甚至破坏性的事件,例如火灾,这在现实生活中是不可能模拟的。在2019冠状病毒病大流行期间实施的限制和对隐私的担忧增加了挑战。我们的研究利用虚幻引擎在虚拟建筑的背景下模拟一些典型的可疑和入侵场景,具有逼真的品质。附加程序用于将视频流从虚拟PTZ摄像机传输到Milestone视频管理系统,并使用户能够从图形客户端应用程序控制这些摄像机。虚拟传感器,如火灾报警器、温度传感器和门禁控制的实现方式类似,实现与现实传感器相同的可编程VMS接口。由于该仿真系统的可扩展性和可重复性,我们对该统一的物理威胁监控系统进行了巩固,并验证了其有效性和用户友好性。在本研究期间开发的模拟虚幻场景和软件附加组件都是高度调制的,因此可以在该领域的未来项目中重用。
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