  In today's Human-Robot Interaction (HRI) scenarios, a prevailing tendency
exists to assume that the robot shall cooperate with the closest individual or
that the scene involves merely a singular human actor. However, in realistic
scenarios, such as shop floor operations, such an assumption may not hold and
personalized target recognition by the robot in crowded environments is
required. To fulfil this requirement, in this work, we propose a person
re-identification module based on continual visual adaptation techniques that
ensure the robot's seamless cooperation with the appropriate individual even
subject to varying visual appearances or partial or complete occlusions. We
test the framework singularly using recorded videos in a laboratory environment
and an HRI scenario, i.e., a person-following task by a mobile robot. The
targets are asked to change their appearance during tracking and to disappear
from the camera field of view to test the challenging cases of occlusion and
outfit variations. We compare our framework with one of the state-of-the-art
Multi-Object Tracking (MOT) methods and the results show that the CARPE-ID can
accurately track each selected target throughout the experiments in all the
cases (except two limit cases). At the same time, the s-o-t-a MOT has a mean of
4 tracking errors for each video.
