Person Re-Identification for Robot Person Following with Online Continual Learning

TL;DR

This paper introduces an online continual learning framework for person re-identification in robot following tasks, enabling better re-identification under occlusion, viewpoint changes, and lighting variations.

Contribution

It proposes a novel online continual learning approach that updates the feature extractor during robot person following, improving re-identification accuracy in dynamic environments.

Findings

Outperforms state-of-the-art methods in re-identification accuracy.

Effectively handles appearance changes and distractors.

Maintains re-identification performance during occlusions and re-appearances.

Abstract

Robot person following (RPF) is a crucial capability in human-robot interaction (HRI) applications, allowing a robot to persistently follow a designated person. In practical RPF scenarios, the person can often be occluded by other objects or people. Consequently, it is necessary to re-identify the person when he/she reappears within the robot's field of view. Previous person re-identification (ReID) approaches to person following rely on a fixed feature extractor. Such an approach often fails to generalize to different viewpoints and lighting conditions in practical RPF environments. In other words, it suffers from the so-called domain shift problem where it cannot re-identify the person when his re-appearance is out of the domain modeled by the fixed feature extractor. To mitigate this problem, we propose a ReID framework for RPF where we use a feature extractor that is optimized online with both short-term and long-term experiences (i.e., recently and previously observed samples during RPF) using the online continual learning (OCL) framework. The long-term experiences are maintained by a memory manager to enable OCL to update the feature extractor. Our experiments demonstrate that even in the presence of severe appearance changes and distractions from visually similar people, the proposed method can still re-identify the person more accurately than the state-of-the-art methods.