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

Chuhao Qin; Lukas Esterle; Evangelos Pournaras

cs.CRMar 24, 2026

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

Authors: Chuhao Qin, Lukas Esterle, Evangelos Pournaras

Organizations: School of Computer Science, University of Leeds, UK · Department of Electrical and Computer Engineering, Aarhus University, Denmark

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.

Figures & tables

Attributes	I-EPOS [ 7 , 8 ]	EPOS [ 9 ]	COHDA [ 10 ]	GGV [ 11 ]
Computational cost	agent: $O(KL)$ ; system: $O(KL\ log\ U)$	agent: $O(K^{C})$ ; system: $O(K^{C}\ log\ U)$	agent: $O(KL)$ ; system: $O(KL)$	agent: $O(KL)$ ; system: $O(KLU)$
Communication cost	agent: $O(L)$ ; system: $O(L\ log\ U)$	agent: $O(K)$ ; system: $O(K\ log\ U)$	agent: $O(UL)$ ; system: $O(UL)$	agent: $O(UL)$ ; system: $O(UL)$
Scalability	✓	✓	$\times$	$\times$
Privacy awareness	✓	$\times$	$\times$	$\times$
$K$ : number of plans (options), $L$ : number of iterations, $C$ : number of children, $U$ : number of agents
Criteria covered $\checkmark$ or not $\times$

TABLE I: Comparison of approaches for discrete-choice combinatorial optimization problem.

Fig. 1: Overview of the smart camera coordination model. Camera A has three orientation plans ( $30^{\circ}$ , $210^{\circ}$ , and $300^{\circ}$ ). When camera A rotates $30^{\circ}$ , its field of view (FoV) covers $40\%$ of cell $a_{2}$ , $70\%$ of cell $a_{3}$ and $10\%$ of cell $a_{4}$ . It generates all possible $360^{\circ}$ plans and then coordinates with other cameras to observe the required regions while excluding the private regions through collective learning. The objective is to match the overall coverage to the target at each cell.

Fig. 2: The coverage value per cell required by the targets with and without private regions.

Fig. 3: Coverage inefficiency of three approaches with different types of camera placement and number of plans per camera. The shadow represents the standard error of I-EPOS . The heatmaps show the coverage performance (match, overlap, or loss) on the 2D map through different methods.

Fig. 4: The coverage performance comparison of different approaches using $10\times 10$ cameras (each with $90$ plans) in the maps with three types of private regions. The metrics include coverage inefficiency, privacy violation rate, and total coverage ratio.

Fig. 5: Comparison of interpolated total cost and coverage performance among different types of camera placement. Each type of camera placement requires smart cameras with different horizontal angles and prices according to FIXr.

Video Surveillance

Video surveillance uses computer vision and multimodal AI to detect, track, explain, and retrieve events in camera streams while balancing reliability, efficiency, and privacy.

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City-scale person re-identification across distributed cameras must handle severe appearance changes from viewpoint, occlusion, and domain shift while complying with data protection rules that prevent sharing raw imagery. We introduce CityGuard, a topology-aware transformer for privacy-preserving identity retrieval in decentralized surveillance. The framework integrates three components. A dispersion-adaptive metric learner adjusts instance-level margins according to feature spread, increasing intra-class compactness. Spatially conditioned attention injects coarse geometry, such as GPS or deployment floor plans, into graph-based self-attention to enable projectively consistent cross-view alignment using only coarse geometric priors without requiring survey-grade calibration. Differentially private embedding maps are coupled with compact approximate indexes to support secure and cost-efficient deployment. Together these designs produce descriptors robust to viewpoint variation, occlusion, and domain shifts, and they enable a tunable balance between privacy and utility under rigorous differential-privacy accounting. Experiments on Market-1501 and additional public benchmarks, complemented by database-scale retrieval studies, show consistent gains in retrieval precision and query throughput over strong baselines, confirming the practicality of the framework for privacy-critical urban identity matching.

Abstract

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