Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing

• URL: http://arxiv.org/abs/2512.09463v1
• Date: Wed, 10 Dec 2025 09:33:03 GMT
• Title: Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing
• Authors: Sander De Coninck, Emilio Gamba, Bart Van Doninck, Abdellatif Bey-Temsamani, Sam Leroux, Pieter Simoens,
• Abstract summary: This paper presents its first comprehensive validation on real-world data collected by industrial partners.<n>We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment.
• Score: 8.54096163251504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed privacy-preserving framework, this paper presents its first comprehensive validation on real-world data collected directly by industrial partners in active production environments. We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment. The approach employs learned visual transformations that obscure sensitive or task-irrelevant information while retaining features essential for task performance. Through both quantitative evaluation of the privacy-utility trade-off and qualitative feedback from industrial partners, we assess the framework's effectiveness, deployment feasibility, and trust implications. Results demonstrate that task-specific obfuscation enables effective monitoring with reduced privacy risks, establishing the framework's readiness for real-world adoption and providing cross-domain recommendations for responsible, human-centric AI deployment in industry.

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