Person Re-Identification for Robot Person Following with Online Continual Learning
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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 often be occluded by other objects or people. Consequently, it is necessary to re-identify the person when he/she re-appears within the robot's field of view. Previous person re-identification (ReID) approaches to person following rely on offline-trained features and short-term experiences. Such an approach i) has a limited capacity to generalize across scenarios; and ii) often fails to re-identify the person when his re-appearance is out of the learned domain represented by the short-term experiences. Based on this observation, in this work, we propose a ReID framework for RPF that leverages long-term experiences. The experiences are maintained by a loss-guided keyframe selection strategy, to enable online continual learning of the appearance model. 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.
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