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

Hanjing Ye1,2; Jieting Zhao1,2; Yu Zhan1,2; Weinan Chen3; Li He1,2; Hong Zhang1,2

2024

10.1109/LRA.2024.3438042

IEEE Robotics and Automation Letters   影响因子和分区

2377-3774

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.

EI

第一

1.Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, Shenzhen, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
3.Guangdong University of Technology, Guangzhou, China

Hanjing Ye,Jieting Zhao,Yu Zhan,et al. Person Re-Identification for Robot Person Following with Online Continual Learning[J]. IEEE Robotics and Automation Letters,2024,PP(99).

Hanjing Ye,Jieting Zhao,Yu Zhan,Weinan Chen,Li He,&Hong Zhang.(2024).Person Re-Identification for Robot Person Following with Online Continual Learning.IEEE Robotics and Automation Letters,PP(99).

Hanjing Ye,et al."Person Re-Identification for Robot Person Following with Online Continual Learning".IEEE Robotics and Automation Letters PP.99(2024).