隐私感知智能摄像头：通过社会责任协调实现视图覆盖
Privacy-Aware Smart Cameras: View Coverage via Socially Responsible Coordination
AI总结：
本文提出一种去中心化框架，使智能摄像头通过集体学习自主选择方向，以在不侵犯隐私的情况下提高公共区域的监控效率。
摘要：
通过隐私感知的智能摄像头实现视图覆盖的协调是实现更社会负责的城市智能的关键。本文提出了一种去中心化框架，其中交互式智能摄像头通过集体学习自主选择其方向，同时通过软约束和硬约束满足来消除隐私违规。该方法可扩展到数百至上千台摄像头，无需集中控制。实验结果表明，与基线和其他最先进的方法相比，覆盖效率提高了18.42%，隐私违规降低了85.53%。这一显著进展进一步揭示了运营商和政策制定者在大规模和敏感城市环境中，如何通过视野、空间布置和预算影响覆盖效率和隐私保护。
英文摘要：
Coordination of view coverage via privacy-aware smart cameras is key to a more socially responsible urban intelligence. Rather than maximizing view coverage at any cost or over relying on expensive cryptographic techniques, we address how cameras can coordinate to legitimately monitor public spaces while excluding privacy-sensitive regions by design. This article proposes a decentralized framework in which interactive smart cameras coordinate to autonomously select their orientation via collective learning, while eliminating privacy violations via soft and hard constraint satisfaction. The approach scales to hundreds up to thousands of cameras without any centralized control. Experimental evidence shows 18.42% higher coverage efficiency and 85.53% lower privacy violation than baselines and other state-of-the-art approaches. This significant advance further unravels practical guidelines for operators and policymakers: how the field of view, spatial placement, and budget of cameras operating by ethically-aligned artificial intelligence jointly influence coverage efficiency and privacy protection in large-scale and sensitive urban environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
