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Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition

In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation...

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Detalles Bibliográficos
Autores principales: Chao, Xin, Hou, Zhenjie, Liang, Jiuzhen, Yang, Tianjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571226/
https://www.ncbi.nlm.nih.gov/pubmed/32932774
http://dx.doi.org/10.3390/s20185180
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author Chao, Xin
Hou, Zhenjie
Liang, Jiuzhen
Yang, Tianjin
author_facet Chao, Xin
Hou, Zhenjie
Liang, Jiuzhen
Yang, Tianjin
author_sort Chao, Xin
collection PubMed
description In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation, called Motion Collaborative Spatio-Temporal Vector (MCSTV) and Motion Spatio-Temporal Map (MSTM). MCSTV comprehensively considers the integral and cooperative between the motion joints. MCSTV weighted accumulates limbs’ motion vector to form a new vector to account for the movement features of human action. To describe the action more comprehensively and accurately, we extract key motion energy by key information extraction based on inter-frame energy fluctuation, project the energy to three orthogonal axes and stitch them in temporal series to construct the MSTM. To combine the advantages of MSTM and MCSTV, we propose Multi-Target Subspace Learning (MTSL). MTSL projects MSTM and MCSTV into a common subspace and makes them complement each other. The results on MSR-Action3D and UTD-MHAD show that our method has higher recognition accuracy than most existing human action recognition algorithms.
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spelling pubmed-75712262020-10-28 Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition Chao, Xin Hou, Zhenjie Liang, Jiuzhen Yang, Tianjin Sensors (Basel) Article In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation, called Motion Collaborative Spatio-Temporal Vector (MCSTV) and Motion Spatio-Temporal Map (MSTM). MCSTV comprehensively considers the integral and cooperative between the motion joints. MCSTV weighted accumulates limbs’ motion vector to form a new vector to account for the movement features of human action. To describe the action more comprehensively and accurately, we extract key motion energy by key information extraction based on inter-frame energy fluctuation, project the energy to three orthogonal axes and stitch them in temporal series to construct the MSTM. To combine the advantages of MSTM and MCSTV, we propose Multi-Target Subspace Learning (MTSL). MTSL projects MSTM and MCSTV into a common subspace and makes them complement each other. The results on MSR-Action3D and UTD-MHAD show that our method has higher recognition accuracy than most existing human action recognition algorithms. MDPI 2020-09-11 /pmc/articles/PMC7571226/ /pubmed/32932774 http://dx.doi.org/10.3390/s20185180 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chao, Xin
Hou, Zhenjie
Liang, Jiuzhen
Yang, Tianjin
Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition
title Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition
title_full Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition
title_fullStr Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition
title_full_unstemmed Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition
title_short Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition
title_sort integrally cooperative spatio-temporal feature representation of motion joints for action recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571226/
https://www.ncbi.nlm.nih.gov/pubmed/32932774
http://dx.doi.org/10.3390/s20185180
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