Robot Person Following Under Partial Occlusion

TL;DR

This paper introduces a novel method for robot person following under partial occlusion using visible joints to estimate the person's location, improving reliability over existing methods in limited field-of-view scenarios.

Contribution

The paper proposes a joint-based localization approach for person following under partial occlusion, addressing a key limitation of existing full-observation assumptions.

Findings

Outperforms state-of-the-art methods under partial occlusion

Demonstrates effectiveness in real robot experiments

Improves reliability of person tracking in limited view conditions

Abstract

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.