  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.
