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

TL;DR

This paper validates a privacy-preserving computer vision framework in real industrial settings, balancing operational needs with worker privacy across three use cases, and demonstrates its effectiveness and deployment feasibility.

Contribution

It provides the first comprehensive real-world validation of a learned visual transformation framework for privacy-preserving industrial computer vision applications.

Findings

Effective privacy-utility trade-off demonstrated

Framework suitable for real-world deployment

Positive feedback from industrial partners

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.