Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging. This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camera-based system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediately. The paper highlights and addresses different algorithmic and system design challenges to guarantee real-time high-accuracy video analytics processing in the testbed. It also presents an example of cloud system infrastructure and a mobile application for real-time notification to keep students, faculty/staff, and responsible security personnel in the loop. At the same time, it covers the design decision to maintain communities' privacy and ethical requirements as well as hardware configuration and setups. We evaluate the system's performance using throughput and end-to-end latency. The experiment results show that, on average, our system's end-to-end latency to notify the end users in case of detecting suspicious objects is 5.3, 5.78, and 11.11 seconds when running 1, 4, and 8 cameras, respectively. On the other hand, in case of detecting anomalous behaviors, the system could notify the end users with 7.3, 7.63, and 20.78 seconds average latency. These results demonstrate that the system effectively detects and notifies abnormal behaviors and suspicious objects to the end users within a reasonable period. The system can run eight cameras simultaneously at a 32.41 Frame Per Second (FPS) rate.

翻译：智能视频监控系统在保障公共安全方面日益重要，尤其在智慧城市中。然而，将实时人工智能技术与低延迟通知报警相结合，使得这类系统的部署极具挑战性。本文基于一所社区学院的真实测试平台，提出了一项关于智能视频监控系统设计与部署的案例研究。我们主要聚焦于一套基于智能摄像头的系统，该系统能够识别可疑/异常活动，并立即向利益相关方和居民发出警报。本文着重阐述并解决了为确保测试平台中实时高精度视频分析处理所面临的不同算法与系统设计挑战。同时，本文还介绍了一个云系统基础设施实例以及一款用于实时通知的移动应用程序，以便将学生、教职员工及负责安保的人员纳入信息闭环。此外，本文涵盖了在维护社区隐私与伦理要求方面的设计决策，以及硬件配置与设置。我们通过吞吐量和端到端延迟来评估系统性能。实验结果表明，在分别运行1、4和8个摄像头时，系统在检测到可疑物体后通知最终用户的平均端到端延迟分别为5.3秒、5.78秒和11.11秒。另一方面，在检测到异常行为时，系统能够以平均7.3秒、7.63秒和20.78秒的延迟通知最终用户。这些结果表明，该系统能够在合理时间内有效检测异常行为和可疑物体并通知最终用户。该系统能够同时以32.41帧/秒的速率运行八个摄像头。