A comparison of human skeleton extractors for real-time human-robot interaction

被引：1
|
作者：
Li, Wanchen [1 ]
Passama, Robin [1 ]
Bonnet, Vincent [2 ]
Cherubini, Andrea [1 ]
机构：
[1] Univ Montpellier, CNRS, Interact Digital Humans Grp, LIRMM, 161 Rue Ada, F-34095 Montpellier, France
[2] INSA Toulouse, LAAS CNRS, 7 Ave Colonel Roche, F-31400 Toulouse, France
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND ITS SOCIAL IMPACTS, ARSO | 2023年
关键词：
human-robot interaction; activity recognition; 3D skeleton detection; computer vision;
D O I：
10.1109/ARSO56563.2023.10187411
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Modern industrial manufacturing procedures gradually integrate physical Human-Robot interaction (pHRI) scenarios. This requires robots to understand human intentions for effective and safe cooperation. Vision is the most commonly used sensor modality for robots to perceive human behavior. In this paper, we compare various vision-based human skeleton extraction frameworks, to provide guidance for the design of human-robot interaction applications. We run various skeleton extractors on a video of a person working with the help of a dual-arm collaborative robot, in a scenario simulating a typical human-robot workspace. By comparing the outcomes of the various skeleton extractors, we justify our choices according to pHRI constraints.
引用
收藏
页码：159 / 165
页数：7
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