Open AccessMulti‐feature consultation model for human action recognition in depth video sequence
Author(s) -
Liu Xueping,
Li Yibo,
Li Xiaoming,
Tian Can,
Yang Yueqi
Publication year - 2018
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8301
Subject(s) - computer science , sequence (biology) , artificial intelligence , feature (linguistics) , field (mathematics) , action (physics) , identification (biology) , feature vector , action recognition , pattern recognition (psychology) , computer vision , variety (cybernetics) , mathematics , class (philosophy) , linguistics , philosophy , genetics , physics , botany , quantum mechanics , pure mathematics , biology
In the field of computer vision research, the research on human action recognition of depth video sequence is an important research direction. Herein, considering the characteristics of temporal and spatial depth video sequence, the authors propose a framework of the consultation model of several action sequence features to solve the classification problem in‐depth video sequence. According to the characteristics of the 3D human action space, a variety of action sequence feature data is obtained, and then these data is projected to three coordinate planes, the acquired fusion features are used to train the consultation model, and finally the model is validated through the data. The authors have achieved good results by comparing the two publicly available datasets with the other methods. Experimental results demonstrate that the model performs well in existing identification methods.