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.

本文介绍了一个基于人工智能的智能视频监控系统，旨在提高社区空间（如教育、休闲区域和小型企业）的安全性。该系统采用元数据而非像素数据进行活动识别，注重隐私，并具备基于云端基础架构和移动应用的实时警报功能。此外，该文还首次对全面的实时异常检测系统进行了评估，用于识别潜在的公共安全事件。通过在社区学院中的实施，作者验证了系统的鲁棒性和性能，提供了关于吞吐量、延迟和伸缩性的实际数据。

智能视频监控在社区空间的综合评估：将人工智能融入闭路电视系统

Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart  Video Surveillance in Community Space

Patients being admitted to a hospital will most often be associated with a
certain clinical development during their stay. However, there is always a risk
of patients being subject to the wrong diagnosis or to a certain treatment not
pertaining to the desired effect, potentially leading to adverse events. Our
research aims to develop an anomaly detection system for identifying deviations
from expected clinical trajectories. To address this goal we analyzed 16 months
of vital sign recordings obtained from the Nordland Hospital Trust (NHT). We
employed an self-supervised framework based on the STraTS transformer
architecture to represent the time series data in a latent space. These
representations were then subjected to various clustering techniques to explore
potential patient phenotypes based on their clinical progress. While our
preliminary results from this ongoing research are promising, they underscore
the importance of enhancing the dataset with additional demographic information
from patients. This additional data will be crucial for a more comprehensive
evaluation of the method's performance.

我们的研究旨在开发一种异常检测系统，以识别与预期临床发展不符的偏差。通过分析挪威北部医院信托公司（NHT）获得的 16 个月的生命体征记录，我们采用基于 STraTS 变压器架构的自监督框架来表示时间序列数据。然后，利用各种聚类技术对这些表示进行处理，以探索基于患者临床进展的潜在表型。尽管我们正在进行的研究的初步结果是令人鼓舞的，但它们强调了从患者获得的额外人口统计学信息对方法性能更全面的评估的重要性。