Human following for mobile robots.pdf (4.58 MB)
Human following for mobile robots
conference contribution
posted on 2022-07-20, 10:11 authored by Wenjuan ZhouWenjuan Zhou, Peter DickensonPeter Dickenson, Haibin CaiHaibin Cai, Baihua LiBaihua LiHuman 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 IPages
660 - 668Source
International Conference on Intelligent Robotics and Applications (ICIRA 2022)Publisher
SpringerVersion
- AM (Accepted Manuscript)
Rights holder
© The Author(s), under exclusive license to Springer Nature Switzerland AGPublisher statement
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-termsAcceptance date
2022-06-13Publication date
2022-08-04Copyright date
2022ISBN
9783031138430; 9783031138447ISSN
0302-9743eISSN
1611-3349Publisher version
Book series
Lecture Notes in Computer Science (LNCS, volume 13455)Language
- en