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.

翻译：隐私感知智能摄像头的视野覆盖协调是实现更具社会责任的城市智能化的关键。我们并非不计代价地最大化视野覆盖或过度依赖昂贵的加密技术，而是探讨摄像头如何通过设计在合法监控公共空间的同时排除隐私敏感区域。本文提出一种去中心化框架，其中交互式智能摄像头通过集体学习自主选择朝向，同时通过软约束与硬约束满足消除隐私侵犯。该方法可扩展至数百乃至数千个摄像头，无需任何集中控制。实验证据表明，与基线及当前最优方法相比，该方法覆盖效率提升18.42%，隐私侵犯降低85.53%。这一重要进展进一步为操作人员与政策制定者揭示了实践指南：在符合伦理规范的人工智能驱动下，摄像头的视场角、空间布局与预算如何协同影响大规模敏感城市环境中的覆盖效率与隐私保护。