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This article adopts and evaluates an AI-enabled Smart Video Solution (SVS)designed to enhance safety in the real world. The system integrates withexisting infrastructure camera networks, leveraging recent advancements in AIfor easy adoption. Prioritizing privacy and ethical standards, pose based datais used for downstream AI tasks such as anomaly detection. Cloud-basedinfrastructure and mobile app are deployed, enabling real-time alerts withincommunities. The SVS employs innovative data representation and visualizationtechniques, such as the Occupancy Indicator, Statistical Anomaly Detection,Bird's Eye View, and Heatmaps, to understand pedestrian behaviors and enhancepublic safety. Evaluation of the SVS demonstrates its capacity to convertcomplex computer vision outputs into actionable insights for stakeholders,community partners, law enforcement, urban planners, and social scientists.This article presents a comprehensive real-world deployment and evaluation ofthe SVS, implemented in a community college environment across 16 cameras. Thesystem integrates AI-driven visual processing, supported by statisticalanalysis, database management, cloud communication, and user notifications.Additionally, the article evaluates the end-to-end latency from the moment anAI algorithm detects anomalous behavior in real-time at the camera level to thetime stakeholders receive a notification. The results demonstrate the system'srobustness, effectively managing 16 CCTV cameras with a consistent throughputof 16.5 frames per second (FPS) over a 21-hour period and an average end-to-endlatency of 26.76 seconds between anomaly detection and alert issuance.

From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety