Self-Supervised Uncalibrated Multi-View Video Anonymization in the Operating Room

• URL: http://arxiv.org/abs/2602.02850v2
• Date: Mon, 09 Feb 2026 17:27:39 GMT
• Title: Self-Supervised Uncalibrated Multi-View Video Anonymization in the Operating Room
• Authors: Keqi Chen, Vinkle Srivastav, Armine Vardazaryan, Cindy Rolland, Didier Mutter, Nicolas Padoy,
• Abstract summary: We propose a novel multi-view video anonymization framework consisting of whole-body person detection and whole-body pose estimation.<n>Our core strategy is to enhance the single-view detector by "retrieving" false negatives using temporal and multi-view context.<n>Experiments on the 4D-OR dataset of simulated surgeries and our dataset of real surgeries show the effectiveness of our approach achieving over 97% recall.
• Score: 11.757426920071175
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Privacy preservation is a prerequisite for using video data in Operating Room (OR) research. Effective anonymization relies on the exhaustive localization of every individual; even a single missed detection necessitates extensive manual correction. However, existing approaches face two critical scalability bottlenecks: (1) they usually require manual annotations of each new clinical site for high accuracy; (2) while multi-camera setups have been widely adopted to address single-view ambiguity, camera calibration is typically required whenever cameras are repositioned. To address these problems, we propose a novel self-supervised multi-view video anonymization framework consisting of whole-body person detection and whole-body pose estimation, without annotation or camera calibration. Our core strategy is to enhance the single-view detector by "retrieving" false negatives using temporal and multi-view context, and conducting self-supervised domain adaptation. We first run an off-the-shelf whole-body person detector in each view with a low-score threshold to gather candidate detections. Then, we retrieve the low-score false negatives that exhibit consistency with the high-score detections via tracking and self-supervised uncalibrated multi-view association. These recovered detections serve as pseudo labels to iteratively fine-tune the whole-body detector. Finally, we apply whole-body pose estimation on each detected person, and fine-tune the pose model using its own high-score predictions. Experiments on the 4D-OR dataset of simulated surgeries and our dataset of real surgeries show the effectiveness of our approach achieving over 97% recall. Moreover, we train a real-time whole-body detector using our pseudo labels, achieving comparable performance and highlighting our method's practical applicability. Code will be available at https://github.com/CAMMA-public/OR_anonymization.

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