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

TL;DR

This paper explores advanced data visualization techniques to improve situational awareness in AI-driven video surveillance, demonstrating how intuitive visual tools can enhance safety, urban planning, and emergency response.

Contribution

It introduces novel visualization tools for AI surveillance data, translating complex computer vision outputs into actionable insights for stakeholders.

Findings

Visualization improves emergency response efficiency

Tools like Heatmaps and Anomaly Detection aid in crowd management

Enhanced data interpretation supports urban development decisions

Abstract

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.