论文标题
人类对保护隐私的低分辨率深度图像的姿势估计
Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images
论文作者
论文摘要
人体姿势估计（HPE）是用于在手术室内开发基于AI的上下文感知系统（OR）的关键构件。但是，即使在RGB-D传感器捕获的深度图像的情况下，也可能会引起24/7的图像的24/7使用，这可能引起人们对隐私的担忧。能够仅使用低分辨率保护隐私图像将解决这些问题，并有助于扩大依靠此类数据的计算机辅助方法，以更大数量的ORS。在本文中，我们在低分辨率深度图像上介绍了HPE的问题，并提出了一种端到端解决方案，该解决方案将多规模的超分辨率网络与2D人体姿势估计网络集成在一起。通过利用以不同超分辨率生成的中间特征映射，我们的方法可以在低分辨率图像（大小为64x48）的低分辨率图像上实现与经过训练和测试的方法（大小为640x480）的方法相等的低分辨率图像。
Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR). The 24/7 use of images coming from cameras mounted on the OR ceiling can however raise concerns for privacy, even in the case of depth images captured by RGB-D sensors. Being able to solely use low-resolution privacy-preserving images would address these concerns and help scale up the computer-assisted approaches that rely on such data to a larger number of ORs. In this paper, we introduce the problem of HPE on low-resolution depth images and propose an end-to-end solution that integrates a multi-scale super-resolution network with a 2D human pose estimation network. By exploiting intermediate feature-maps generated at different super-resolution, our approach achieves body pose results on low-resolution images (of size 64x48) that are on par with those of an approach trained and tested on full resolution images (of size 640x480).
