Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart
  Video Surveillance in Community Space
Shanle Yao Babak Rahimi Ardabili Armin Danesh Pazho Ghazal Alinezhad Noghre Christopher Neff Hamed Tabkhi
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.