This article proposes a novel ultralarge-scale crowd monitoring system, namely, the ULCM system. The ULCM system enables advanced sensing and networking technologies aimed at collecting and processing multimodal, multiperspective, and real-time crowding data relevant to crowd management. This data will be further analyzed to provide a global realization of evolving events over a large geographical area as they occur in real time. The ULCM is the infrastructure component of an intelligent platform that is being developed by our research group to provide crowd intelligence to decision makers through an interactive digitized visual environment. In order to achieve a full comprehensive scene overview, the ULCM deployment utilizes a multiplicity of unmanned aerial vehicle (UAV) agents in different operational scenarios. The aerial deployment and control are realized by custom multiple UAV networks and airborne LiDAR sensors. The deployment and control on the ground sensory agents are based on multiple subnetworks, including closed-circuit television (CCTV) and infrared gas and ultrasonic sensors networks. Eventually, ULCM employs the software-defined network (SDN) and edge cloud technologies to optimize the networking and data analytics performance from the perspective of infrastructure.

中文翻译：

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超大规模人群监控系统架构与设计问题


本文提出了一种新型的超大规模人群监控系统，即ULCM系统。 ULCM 系统支持先进的传感和网络技术，旨在收集和处理与人群管理相关的多模式、多视角和实时人群数据。这些数据将被进一步分析，以提供大地理区域内实时发生的不断变化的事件的全球认识。 ULCM 是我们的研究小组正在开发的智能平台的基础设施组件，旨在通过交互式数字化视觉环境为决策者提供群体智能。为了实现全面的场景概览，ULCM 部署在不同的操作场景中使用了多个无人机 (UAV) 代理。空中部署和控制是通过定制的多个无人机网络和机载激光雷达传感器实现的。地面传感代理的部署和控制基于多个子网络，包括闭路电视（CCTV）以及红外气体和超声波传感器网络。最终，ULCM采用软件定义网络（SDN）和边缘云技术，从基础设施的角度优化网络和数据分析性能。