🤖 AI Summary
This study addresses the challenge of jointly optimizing AI-powered video analytics and privacy preservation in community security. Methodologically, it proposes a lightweight, interpretable, privacy-by-design Smart Video Solution (SVS) that replaces raw video transmission with anonymized skeletal pose features extracted via edge-deployed lightweight pose estimation, coupled with statistical time-series anomaly detection. Novel visualization paradigms—including an Occupancy Indicator and bird’s-eye-view heatmaps—are introduced to support multi-stakeholder decision-making (e.g., law enforcement response and urban planning). An edge–cloud collaborative architecture integrates time-series databases and real-time notification services. Deployed across 16 CCTV channels, the system operated stably for 21 hours, achieving 16.5 FPS throughput and an end-to-end latency of 26.76 seconds—demonstrating robustness and practical feasibility. The core contributions are (1) the first privacy-enhanced behavioral understanding framework for community surveillance, and (2) a governance-oriented visual insight translation mechanism.
📝 Abstract
This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird's Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex 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 of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system's robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance.
