The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While camera-equipped drones provide control over this viewpoint, automatically positioning them at the location which will yield the highest accuracy remains an open problem. This is the problem that we address in this paper. Specifically, given a short video sequence, we introduce an algorithm that predicts the where a drone should go in the future frame so as to maximize 3D human pose estimation accuracy. A key idea underlying our approach is a method to estimate the uncertainty of the 3D body pose estimates. We integrate several sources of uncertainty, originating from a deep learning based regressors and temporal smoothness. The resulting motion planner leads to improved 3D body pose estimates and outperforms or matches existing planners that are based on person following and orbiting.

本论文提出一种基于算法规划的视角控制方法，通过估算3D人体姿势估计的不确定性，结合基于深度学习的回归器和时间平滑性等多个不确定性来源，实现了更为准确的3D姿态估计，其性能优于基于人物跟随和围绕的相关研究。

ActiveMoCap: 用于主动人体运动捕捉的优化视角选择