Nonnegative Tensor-Based Linear Dynamical Systems for Recognizing Human Action from 3D Skeletons
Joint Authors
Li, Guang
Liu, Kai
Ding, Wenwen
Cheng, Fei
Ding, Chongyang
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-27
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Recently, skeleton-based action recognition has become a very important topic in the field of computer vision.
It is a challenging task to accurately build a human action model and precisely distinguish similar human actions.
In this paper, an action (skeleton sequence) is represented as a third-order nonnegative tensor time series to capture the original spatiotemporal information of the action.
As a linear dynamical system (LDS) is an efficient tool for encoding the spatiotemporal data in various disciplines, this paper proposes a nonnegative tensor-based LDS (nLDS) to model the third-order nonnegative tensor time series.
Nonnegative Tucker decomposition (NTD) is utilized to estimate the parameters of the nLDS model.
These parameters are used to build extended observability sequence O∞T for the action, which implies that O∞T can be considered as the feature descriptor of the action.
To avoid the limitations introduced by approximating O∞T with a finite-order matrix, we represent an action as a point on infinite Grassmann manifold comprising the orthonormalized extended observability sequences.
The classification task can be performed by dictionary learning and sparse coding on the infinite Grassmann manifold.
The experimental results on the MSR-Action3D, UTKinect-Action, and G3D-Gaming datasets demonstrate that the proposed approach achieves a better performance in comparison with the state-of-the-art methods.
American Psychological Association (APA)
Li, Guang& Liu, Kai& Ding, Wenwen& Cheng, Fei& Ding, Chongyang. 2019. Nonnegative Tensor-Based Linear Dynamical Systems for Recognizing Human Action from 3D Skeletons. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1197901
Modern Language Association (MLA)
Li, Guang…[et al.]. Nonnegative Tensor-Based Linear Dynamical Systems for Recognizing Human Action from 3D Skeletons. Mathematical Problems in Engineering No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1197901
American Medical Association (AMA)
Li, Guang& Liu, Kai& Ding, Wenwen& Cheng, Fei& Ding, Chongyang. Nonnegative Tensor-Based Linear Dynamical Systems for Recognizing Human Action from 3D Skeletons. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1197901
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197901