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.
@article{arxiv.2512.09463,
title = {Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing},
author = {Sander De Coninck and Emilio Gamba and Bart Van Doninck and Abdellatif Bey-Temsamani and Sam Leroux and Pieter Simoens},
journal= {arXiv preprint arXiv:2512.09463},
year = {2025}
}