The pervasive deployment of surveillance cameras produces a massive volume of data, requiring nuanced interpretation. This study thoroughly examines data representation and visualization techniques tailored for AI surveillance data within current infrastructures. It delves into essential data metrics, methods for situational awareness, and various visualization techniques, highlighting their potential to enhance safety and guide urban development. This study is built upon real-world research conducted in a community college environment, utilizing eight cameras over eight days. This study presents tools like the Occupancy Indicator, Statistical Anomaly Detection, Bird's Eye View, and Heatmaps to elucidate pedestrian behaviors, surveillance, and public safety. Given the intricate data from smart video surveillance, such as bounding boxes and segmented images, we aim to convert these computer vision results into intuitive visualizations and actionable insights for stakeholders, including law enforcement, urban planners, and social scientists. The results emphasize the crucial impact of visualizing AI surveillance data on emergency handling, public health protocols, crowd control, resource distribution, predictive modeling, city planning, and informed decision-making.

翻译：监控摄像头的大规模部署产生了海量数据，这需要细致入微的解读。本研究深入探讨了在现有基础设施中，专为人工智能监控数据定制的数据表示与可视化技术。研究涵盖了基本数据指标、态势感知方法以及多种可视化技术，着重阐述了它们提升安全性并指导城市发展的潜力。本研究基于在一所社区大学环境中进行的实际研究，该研究使用八台摄像头，持续八天收集数据。本研究提出了如占用率指示器、统计异常检测、鸟瞰图和热力图等工具，用以阐释行人行为、监控过程与公共安全。鉴于智能视频监控所产生的复杂数据（例如边界框和分割图像），我们致力于将这些计算机视觉结果转化为直观的可视化信息及可操作的洞见，以供执法部门、城市规划者和社会科学家等利益相关方使用。研究结果强调了将人工智能监控数据可视化对应急处理、公共卫生方案、人群管控、资源分配、预测建模、城市规划及知情决策等方面产生的关键性影响。