论文标题
保护人类机器人相互作用的隐私保护姿势估计
Privacy-Preserving Pose Estimation for Human-Robot Interaction
论文作者
论文摘要
姿势估计是非语言人类机器人相互作用的重要技术。也就是说，一个人的空间中存在相机引起了隐私问题，并可能导致机器人不信任。在本文中，我们提出了一种基于隐私的摄像头姿势估计方法。所提出的系统由用户控制的半透明过滤器组成，该过滤器覆盖相机和一个图像增强模块，旨在促进过滤的（阴影）图像中的姿势估算，而从未捕获用户的清晰图像。考虑到与摄像机，背景混乱和膜厚度的距离的影响，我们在新的过滤图像数据集上评估了系统的性能。根据我们的发现，我们得出的结论是，我们的系统可以在有效地检测人类的姿势信息的同时保护人类的隐私。
Pose estimation is an important technique for nonverbal human-robot interaction. That said, the presence of a camera in a person's space raises privacy concerns and could lead to distrust of the robot. In this paper, we propose a privacy-preserving camera-based pose estimation method. The proposed system consists of a user-controlled translucent filter that covers the camera and an image enhancement module designed to facilitate pose estimation from the filtered (shadow) images, while never capturing clear images of the user. We evaluate the system's performance on a new filtered image dataset, considering the effects of distance from the camera, background clutter, and film thickness. Based on our findings, we conclude that our system can protect humans' privacy while detecting humans' pose information effectively.
