从实验室到现场:对人工智能驱动的智能视频解决方案的实际评估,以增强社区安全

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shanle Yao , Babak Rahimi Ardabili , Armin Danesh Pazho , Ghazal Alinezhad Noghre , Christopher Neff , Lauren Bourque , Hamed Tabkhi
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引用次数: 0

摘要

本文采用并评估了一种基于人工智能的智能视频解决方案(SVS),旨在提高现实世界中的安全性。该系统与现有的基础设施摄像机网络集成,利用人工智能的最新进展,使其易于采用。基于姿势的数据优先考虑隐私和道德标准,用于下游人工智能任务,如异常检测。部署了基于云的基础设施和移动应用程序,可以在社区内实现实时警报。SVS采用创新的数据表示和可视化技术,如占用指示器、统计异常检测、鸟瞰图和热图,以了解行人行为并增强公共安全。对SVS的评估表明,它能够将复杂的计算机视觉输出转化为利益相关者、社区合作伙伴、执法部门、城市规划者和社会科学家可操作的见解。本文介绍了SVS在现实世界的全面部署和评估,在一个社区大学环境中实现了16个摄像机。该系统集成了人工智能驱动的视觉处理,支持统计分析、数据库管理、云通信和用户通知。此外,本文还评估了从AI算法在相机级别实时检测异常行为的时刻到利益相关者收到通知的时间的端到端延迟。结果证明了该系统的鲁棒性,在21小时内有效地管理16台CCTV摄像机,保持16.5帧/秒(FPS)的一致吞吐量,异常检测和警报发出之间的平均端到端延迟为26.76秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From lab to field: Real-world evaluation of an AI-driven Smart Video Solution to enhance community safety
This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose-based data is used for downstream AI tasks such as anomaly detection. A Cloud-based infrastructure and a mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird’s Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment with 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system’s robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-h period and an average end-to-end latency of 26.76 s between anomaly detection and alert issuance.
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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