Open AccessA Method for Detecting Interaction between 3D Hands and Unknown Objects in RGB Video
Author(s) -
WU Yan-kun,
Peipei Wang,
Wang Xin,
Guanqun Liu,
Lin Zhao,
Xiang Ji
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012038
Subject(s) - artificial intelligence , computer science , computer vision , rgb color model , classifier (uml) , object (grammar) , artificial neural network , position (finance) , frame (networking) , computer graphics (images) , telecommunications , finance , economics
We propose a model that can extract 3D position of hand and object in per-frame of RGB videos through a single feed-forward neural network and a zero-shot learning classifier, and understand unknown hand-object interactions in the entire video through an interactive temporal module. The process is trained end-to-end, without depth images or annotated coordinates as input, which has good application prospects in real life.