This article presents an AI-enabled Smart Video Surveillance (SVS) designed to enhance safety in community spaces such as educational and recreational areas, and small businesses. The proposed system innovatively integrates with existing CCTV and wired camera networks, simplifying its adoption across various community cases to leverage recent AI advancements. Our SVS system, focusing on privacy, uses metadata instead of pixel data for activity recognition, aligning with ethical standards. It features cloud-based infrastructure and a mobile app for real-time, privacy-conscious alerts in communities. This article notably pioneers a comprehensive real-world evaluation of the SVS system, covering AI-driven visual processing, statistical analysis, database management, cloud communication, and user notifications. It's also the first to assess an end-to-end anomaly detection system's performance, vital for identifying potential public safety incidents. For our evaluation, we implemented the system in a community college, serving as an ideal model to exemplify the proposed system's capabilities. Our findings in this setting demonstrate the system's robustness, with throughput, latency, and scalability effectively managing 16 CCTV cameras. The system maintained a consistent 16.5 frames per second (FPS) over a 21-hour operation. The average end-to-end latency for detecting behavioral anomalies and alerting users was 26.76 seconds.

翻译：本文提出了一种人工智能赋能的智能视频监控系统，旨在提升教育、娱乐场所及小型企业等社区空间的安全性。该创新系统可与现有闭路电视和有线摄像机网络无缝集成，简化了在各类社区场景中应用最新AI技术的门槛。我们的智能视频监控系统注重隐私保护，采用元数据而非像素数据进行行为识别，符合伦理标准。系统配备基于云的基础设施和移动应用程序，可实时发送隐私保护的社区预警通知。本文首次对智能视频监控系统进行全面的真实场景评估，涵盖AI驱动的视觉处理、统计分析、数据库管理、云通信及用户通知等环节，并开创性地评估了端到端异常检测系统的性能——这对识别潜在公共安全事件至关重要。为完成评估，我们在社区学院部署了该系统，将其作为理想模型验证系统能力。实验结果表明，系统具备稳健性，可通过吞吐量、延迟和可扩展性有效管理16路闭路电视摄像头。系统在21小时持续运行中保持了16.5帧/秒的稳定帧率，检测行为异常并通知用户的平均端到端延迟为26.76秒。