Human following is an essential function in many robotic systems. Most of the existing human following algorithms are based on human tracking algorithms. However, in practical scenarios, the human subject might easily disappear due to occlusions and quick movements. In order to solve the problem of occlusion, this paper proposed a classification-based human following framework. After using a pretrained MobileNetV2 model to detect the human subjects, the robot will automatically train a classification model to identify the target person. In the end, the robot is controlled by some rule-based motion commands to follow the target human. Experimental results on several practical scenarios have demonstrated the effectiveness of the algorithm.
History
School
Science
Sport, Exercise and Health Sciences
Department
Computer Science
Published in
Intelligent Robotics and Applications: 15th International Conference, ICIRA 2022, Harbin, China, August 1–3, 2022, Proceedings, Part I
Pages
660 - 668
Source
International Conference on Intelligent Robotics and Applications (ICIRA 2022)
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-13844-7_61. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms