Privacy-Preserving Pose Estimation for Human-Robot Interaction
This addresses privacy issues for users in human-robot interaction, though it appears incremental as it builds on existing pose estimation methods with a new privacy-focused component.
The paper tackles the privacy concerns in camera-based pose estimation for human-robot interaction by proposing a system with a user-controlled translucent filter and image enhancement module, achieving effective pose detection without capturing clear images.
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.