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Jointly Learning Multiple Sequential Dynamics for Human Action Recognition
Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4493153/ https://www.ncbi.nlm.nih.gov/pubmed/26147979 http://dx.doi.org/10.1371/journal.pone.0130884 |
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author | Liu, An-An Su, Yu-Ting Nie, Wei-Zhi Yang, Zhao-Xuan |
author_facet | Liu, An-An Su, Yu-Ting Nie, Wei-Zhi Yang, Zhao-Xuan |
author_sort | Liu, An-An |
collection | PubMed |
description | Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces. For model learning, we propose the multi-task conditional random fields (MTCRFs) model to discover the sequence-specific structure and the sequence-shared relationship. Specifically, the multi-chain graph structure and the corresponding probabilistic model are designed to represent the interaction among multiple part-induced action unit sequences. Moreover we propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D. |
format | Online Article Text |
id | pubmed-4493153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44931532015-07-15 Jointly Learning Multiple Sequential Dynamics for Human Action Recognition Liu, An-An Su, Yu-Ting Nie, Wei-Zhi Yang, Zhao-Xuan PLoS One Research Article Discovering visual dynamics during human actions is a challenging task for human action recognition. To deal with this problem, we theoretically propose the multi-task conditional random fields model and explore its application on human action recognition. For visual representation, we propose the part-induced spatiotemporal action unit sequence to represent each action sample with multiple partwise sequential feature subspaces. For model learning, we propose the multi-task conditional random fields (MTCRFs) model to discover the sequence-specific structure and the sequence-shared relationship. Specifically, the multi-chain graph structure and the corresponding probabilistic model are designed to represent the interaction among multiple part-induced action unit sequences. Moreover we propose the model learning and inference methods to discover temporal context within individual action unit sequence and the latent correlation among different body parts. Extensive experiments are implemented to demonstrate the superiority of the proposed method on two popular RGB human action datasets, KTH & TJU, and the depth dataset in MSR Daily Activity 3D. Public Library of Science 2015-07-06 /pmc/articles/PMC4493153/ /pubmed/26147979 http://dx.doi.org/10.1371/journal.pone.0130884 Text en © 2015 Liu et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Liu, An-An Su, Yu-Ting Nie, Wei-Zhi Yang, Zhao-Xuan Jointly Learning Multiple Sequential Dynamics for Human Action Recognition |
title | Jointly Learning Multiple Sequential Dynamics for Human Action Recognition |
title_full | Jointly Learning Multiple Sequential Dynamics for Human Action Recognition |
title_fullStr | Jointly Learning Multiple Sequential Dynamics for Human Action Recognition |
title_full_unstemmed | Jointly Learning Multiple Sequential Dynamics for Human Action Recognition |
title_short | Jointly Learning Multiple Sequential Dynamics for Human Action Recognition |
title_sort | jointly learning multiple sequential dynamics for human action recognition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4493153/ https://www.ncbi.nlm.nih.gov/pubmed/26147979 http://dx.doi.org/10.1371/journal.pone.0130884 |
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