Privacy-Aware Smart Cameras: View Coverage via Socially Responsible Coordination
This work addresses privacy protection in urban surveillance for operators and policymakers, offering a practical decentralized solution, though it is incremental in applying collective learning to an existing domain.
The paper tackles the problem of coordinating smart cameras to monitor public spaces while excluding privacy-sensitive regions, achieving 18.42% higher coverage efficiency and 85.53% lower privacy violation compared to baselines and state-of-the-art methods.
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.