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

Enhancing Situational Awareness in Surveillance: Leveraging Data Visualization Techniques for Machine Learning-based Video Analytics Outcomes