Privacy-Aware Smart Cameras: View Coverage via Socially Responsible Coordination

TL;DR

This paper introduces a decentralized, privacy-aware coordination framework for smart cameras that optimizes view coverage while respecting privacy constraints, scalable to large urban environments with improved efficiency and privacy protection.

Contribution

It presents a novel decentralized collective learning approach enabling smart cameras to coordinate view coverage with privacy constraints without centralized control.

Findings

18.42% higher coverage efficiency

85.53% lower privacy violations

Scalable to thousands of cameras

Abstract

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.