DisguisOR：手术室的全方位人脸匿名化
DisguisOR: Holistic Face Anonymization for the Operating Room
AI总结：
针对手术室多视角视频中现有2D匿名化因遮挡效果差的问题，提出融合多摄像头RGB-D为3D点云，回归人体网格并对齐面部网格以生成几何一致的3D匿名化，提升人脸定位率并减少对下游任务的损害。
摘要：
目的：手术数据科学（SDS）的最新进展促进了医院环境中视频录制的增加。虽然手术工作流识别等方法在提高患者护理质量方面显示出潜力，但视频数据的数量已超过可以手动匿名化图像的范围。由于遮挡和阻碍，现有的自动2D匿名化方法在手术室（OR）中表现不佳。我们提出使用来自多个摄像头流的3D数据对多视角OR录制进行匿名化。方法：来自多个摄像头的RGB和深度图像被融合为场景的3D点云表示。然后，我们通过将参数化人体网格模型回归到检测到的3D人体关键点上，并将面部网格与融合的3D点云对齐，在3D中检测每个个体的面部。网格模型被渲染到每个采集的摄像头视图中，替换每个个体的面部。结果：我们的方法在定位面部方面显示出比现有方法更高的成功率。DisguisOR为每个摄像头视图生成几何一致的匿名化，实现更逼真的匿名化，对下游任务的损害更小。结论：手术室中频繁的阻碍和拥挤使得现成的匿名化方法仍有很大的改进空间。DisguisOR在场景级别解决隐私问题，并有可能促进SDS的进一步研究。
英文摘要：
Purpose: Recent advances in Surgical Data Science (SDS) have contributed to an increase in video recordings from hospital environments. While methods such as surgical workflow recognition show potential in increasing the quality of patient care, the quantity of video data has surpassed the scale at which images can be manually anonymized. Existing automated 2D anonymization methods under-perform in Operating Rooms (OR), due to occlusions and obstructions. We propose to anonymize multi-view OR recordings using 3D data from multiple camera streams. Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene. We then detect each individual's face in 3D by regressing a parametric human mesh model onto detected 3D human keypoints and aligning the face mesh with the fused 3D point cloud. The mesh model is rendered into every acquired camera view, replacing each individual's face. Results: Our method shows promise in locating faces at a higher rate than existing approaches. DisguisOR produces geometrically consistent anonymizations for each camera view, enabling more realistic anonymization that is less detrimental to downstream tasks. Conclusion: Frequent obstructions and crowding in operating rooms leaves significant room for improvement for off-the-shelf anonymization methods. DisguisOR addresses privacy on a scene level and has the potential to facilitate further research in SDS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
