从实验室到现场：AI驱动的智能视频解决方案以增强社区安全的真实世界评估
From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety
AI总结：
本文评估了一种AI驱动的智能视频解决方案（SVS），通过集成现有摄像头网络和云端基础设施，利用基于姿态的数据进行异常检测，在社区学院环境中实现了16个摄像头的实时监控与警报，系统在21小时内保持16.5 FPS的吞吐量，端到端延迟为26.76秒。
摘要：
本文采用并评估了一种旨在增强现实世界安全性的AI赋能智能视频解决方案（SVS）。该系统与现有基础设施的摄像头网络集成，利用AI的最新进展以便于采用。优先考虑隐私和伦理标准，使用基于姿态的数据进行异常检测等下游AI任务。部署了基于云的基础设施和移动应用程序，实现了社区内的实时警报。SVS采用创新的数据表示和可视化技术，如占用指示器、统计异常检测、鸟瞰图和热力图，以了解行人行为并增强公共安全。对SVS的评估表明，其能够将复杂的计算机视觉输出转化为利益相关者、社区合作伙伴、执法部门、城市规划师和社会科学家的可操作洞察。本文介绍了SVS的综合真实世界部署和评估，该系统在社区学院环境中跨16个摄像头实施。该系统集成了AI驱动的视觉处理，并辅以统计分析、数据库管理、云通信和用户通知。此外，文章评估了从AI算法在摄像头层面实时检测到异常行为的那一刻起，到利益相关者收到通知为止的端到端延迟。结果表明了系统的稳健性，在21小时内有效管理16个CCTV摄像头，吞吐量稳定在16.5帧每秒（FPS），异常检测与警报发布之间的平均端到端延迟为26.76秒。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
