Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912756760838144 |
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| author | De Coninck, Sander Gamba, Emilio Van Doninck, Bart Bey-Temsamani, Abdellatif Leroux, Sam Simoens, Pieter |
| author_facet | De Coninck, Sander Gamba, Emilio Van Doninck, Bart Bey-Temsamani, Abdellatif Leroux, Sam Simoens, Pieter |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_09463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing De Coninck, Sander Gamba, Emilio Van Doninck, Bart Bey-Temsamani, Abdellatif Leroux, Sam Simoens, Pieter Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09463 |