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

TL;DR

This paper introduces a self-supervised multi-view video anonymization framework for operating rooms that does not require manual annotations or camera calibration, improving scalability and accuracy in privacy preservation.

Contribution

The authors propose a novel self-supervised approach that enhances detection and pose estimation without annotations or calibration, enabling scalable anonymization in surgical videos.

Findings

Achieved over 97% recall in detecting individuals in surgical videos.

Effectively recovered false negatives using multi-view and temporal context.

Produced a real-time detector with comparable performance using pseudo labels.

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.