Securing workers and workspaces: Contextual privacy for vision-based ergonomics

Multi-camera computer vision in industry offers advantages but poses risks to worker privacy and intellectual property through exposure of sensitive contextual information. Existing privacy methods often inadequately protect background details crucial in manufacturing. This issue is prominent in applications like automated ergonomic assessment, where visual data for posture analysis can reveal sensitive workplace information. We propose a system for simultaneous personal privacy and enhanced contextual intellectual property protection, featuring a novel probabilistic obfuscation technique. Our edge-based Generative Adversarial Privacy system employs a modified obfuscator that learns to inject controlled, pixel-wise random noise, particularly into non-critical background regions. This more effectively obscures IP-sensitive environmental details before data transmission for central analysis (e.g., pose estimation). Our approach, validated in a multi-camera ergonomic study, effectively protects worker privacy and contextual IP (metrics-evaluated) and maintains 3D pose accuracy for reliable ergonomic assessment. This work provides a solution for deploying vision systems in sensitive industrial settings by holistically addressing privacy requirements through an advanced, adaptive obfuscation strategy.