  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.
