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

arXiv: https://arxiv.org/abs/2602.02850 · HTML: https://mlsanity.com/paper/2602.02850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 30 of 31)

- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [Whole-Body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2007.11858/) (2020)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification](https://mlsanity.com/paper/2302.11813/) (2023)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [Finding Tiny Faces](https://mlsanity.com/paper/1612.04402/) (2016)
- [DEIM: DETR with Improved Matching for Fast Convergence](https://mlsanity.com/paper/2412.04234/) (2024)
- [YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception](https://mlsanity.com/paper/2506.17733/) (2025)
- [MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation](https://mlsanity.com/paper/1808.08180/) (2018)
- [4D-OR: Semantic Scene Graphs for OR Domain Modeling](https://mlsanity.com/paper/2203.11937/) (2022)
- [Live Face De-Identification in Video](https://mlsanity.com/paper/1911.08348/) (2019)
- [SSH: Single Stage Headless Face Detector](https://mlsanity.com/paper/1708.03979/) (2017)
- [CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions](https://mlsanity.com/paper/1910.09031/) (2019)
- [Progressive End-to-End Object Detection in Crowded Scenes](https://mlsanity.com/paper/2203.07669/) (2022)
- [CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection](https://mlsanity.com/paper/2302.06294/) (2023)
- [LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms](https://mlsanity.com/paper/2303.13293/) (2023)
- [Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights](https://mlsanity.com/paper/2407.07586/) (2024)
- [A Robotic 3D Perception System for Operating Room Environment Awareness](https://mlsanity.com/paper/2003.09487/) (2020)
- [Self-supervised Multi-view Person Association and Its Applications](https://mlsanity.com/paper/1805.08717/) (2018)
- [FIVA: Facial Image and Video Anonymization and Anonymization Defense](https://mlsanity.com/paper/2309.04228/) (2023)
- [DisguisOR: Holistic Face Anonymization for the Operating Room](https://mlsanity.com/paper/2307.14241/) (2023)
- [Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach](https://mlsanity.com/paper/1811.12296/) (2018)
- [Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study](https://mlsanity.com/paper/2203.09230/) (2022)
- [Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis](https://mlsanity.com/paper/2207.07894/) (2022)

## Abstract (arXiv)

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 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 99% and 97% recall, respectively. 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.