Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often assume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of his/her joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.

翻译：机器人人员跟随（RPF）是一种支持许多人机交互（HRI）应用的能力。然而，现有的人员跟随解决方案通常假设能完全观测到被跟踪人员。因此，在部分遮挡（即无法满足完全观测假设的情况）下，它们无法可靠地跟踪人员。本文聚焦于单目摄像头视野受限导致部分遮挡条件下的机器人人员跟随问题。基于"当目标人员的一个或多个关节点可见时，仍可定位该人员"这一关键洞察，我们提出了一种方法，使每个可见关节点都能对跟随人员的定位估计做出贡献。在公开的人员跟随数据集上的实验表明，即使存在部分遮挡，所提方法仍能比现有最先进方法更可靠地定位人员。此外，该方法在移动机器人上的实际实验中也展示了其应用效果。