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

Sander De Coninck (UGent) , Emilio Gamba, Bart Van Doninck, Abdellatif Bey-Temsamani, Thorsten Cardoen (UGent) , Sam Leroux (UGent) and Pieter Simoens (UGent)
(2026) COMPUTER VISION AND IMAGE UNDERSTANDING. 265.
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Abstract
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.
Keywords
Contextual privacy protection, Visual privacy, Human pose estimation, Ergonomic analysis, Generative adversarial privacy, Industry 5.0

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MLA
De Coninck, Sander, et al. “Securing Workers and Workspaces : Contextual Privacy for Vision-Based Ergonomics.” COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 265, 2026, doi:10.1016/j.cviu.2026.104675.
APA
De Coninck, S., Gamba, E., Van Doninck, B., Bey-Temsamani, A., Cardoen, T., Leroux, S., & Simoens, P. (2026). Securing workers and workspaces : contextual privacy for vision-based ergonomics. COMPUTER VISION AND IMAGE UNDERSTANDING, 265. https://doi.org/10.1016/j.cviu.2026.104675
Chicago author-date
De Coninck, Sander, Emilio Gamba, Bart Van Doninck, Abdellatif Bey-Temsamani, Thorsten Cardoen, Sam Leroux, and Pieter Simoens. 2026. “Securing Workers and Workspaces : Contextual Privacy for Vision-Based Ergonomics.” COMPUTER VISION AND IMAGE UNDERSTANDING 265. https://doi.org/10.1016/j.cviu.2026.104675.
Chicago author-date (all authors)
De Coninck, Sander, Emilio Gamba, Bart Van Doninck, Abdellatif Bey-Temsamani, Thorsten Cardoen, Sam Leroux, and Pieter Simoens. 2026. “Securing Workers and Workspaces : Contextual Privacy for Vision-Based Ergonomics.” COMPUTER VISION AND IMAGE UNDERSTANDING 265. doi:10.1016/j.cviu.2026.104675.
Vancouver
1.
De Coninck S, Gamba E, Van Doninck B, Bey-Temsamani A, Cardoen T, Leroux S, et al. Securing workers and workspaces : contextual privacy for vision-based ergonomics. COMPUTER VISION AND IMAGE UNDERSTANDING. 2026;265.
IEEE
[1]
S. De Coninck et al., “Securing workers and workspaces : contextual privacy for vision-based ergonomics,” COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 265, 2026.
@article{01KJ54SEE53EKDJFYV0W6HGJV0,
  abstract     = {{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.}},
  articleno    = {{104675}},
  author       = {{De Coninck, Sander and Gamba, Emilio and Van Doninck, Bart and Bey-Temsamani, Abdellatif and Cardoen, Thorsten and Leroux, Sam and Simoens, Pieter}},
  issn         = {{1077-3142}},
  journal      = {{COMPUTER VISION AND IMAGE UNDERSTANDING}},
  keywords     = {{Contextual privacy protection,Visual privacy,Human pose estimation,Ergonomic analysis,Generative adversarial privacy,Industry 5.0}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{Securing workers and workspaces : contextual privacy for vision-based ergonomics}},
  url          = {{http://doi.org/10.1016/j.cviu.2026.104675}},
  volume       = {{265}},
  year         = {{2026}},
}
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