Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework

Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been developed for learning spatiotemporal parameters from...

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Autores principales:	Faisal Mehmood, Enqing Chen, Muhammad Azeem Akbar, Abeer Abdulaziz Alsanad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	human action recognition (HAR) skeleton data spatiotemporal multi-task learning network (MTLN) convolutional neural network (CNN) Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/ef9865d76f654905b711d50b6b7c2dd9
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id	oai:doaj.org-article:ef9865d76f654905b711d50b6b7c2dd9
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spelling	oai:doaj.org-article:ef9865d76f654905b711d50b6b7c2dd92021-11-11T15:42:09ZHuman Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework10.3390/electronics102127082079-9292https://doaj.org/article/ef9865d76f654905b711d50b6b7c2dd92021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2708https://doaj.org/toc/2079-9292Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been developed for learning spatiotemporal parameters from skeleton sequences. However, two critical problems exist: (1) previous skeleton sequences were created by connecting different joints with a static order; (2) earlier methods were not efficient enough to focus on valuable joints. Specifically, this study aimed to (1) demonstrate the ability of convolutional neural networks to learn spatiotemporal parameters of skeleton sequences from different frames of human action, and (2) to combine the process of all frames created by different human actions and fit in the spatial structure information necessary for action recognition, using multi-task learning networks (MTLNs). The results were significantly improved compared with existing models by executing the proposed model on an NTU RGB+D dataset, an SYSU dataset, and an SBU Kinetic Interaction dataset. We further implemented our model on noisy expected poses from subgroups of the Kinetics dataset and the UCF101 dataset. The experimental results also showed significant improvement using our proposed model.Faisal MehmoodEnqing ChenMuhammad Azeem AkbarAbeer Abdulaziz AlsanadMDPI AGarticlehuman action recognition (HAR)skeleton dataspatiotemporalmulti-task learning network (MTLN)convolutional neural network (CNN)ElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2708, p 2708 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	human action recognition (HAR) skeleton data spatiotemporal multi-task learning network (MTLN) convolutional neural network (CNN) Electronics TK7800-8360
spellingShingle	human action recognition (HAR) skeleton data spatiotemporal multi-task learning network (MTLN) convolutional neural network (CNN) Electronics TK7800-8360 Faisal Mehmood Enqing Chen Muhammad Azeem Akbar Abeer Abdulaziz Alsanad Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
description	Human action recognition (HAR) by skeleton data is considered a potential research aspect in computer vision. Three-dimensional HAR with skeleton data has been used commonly because of its effective and efficient results. Several models have been developed for learning spatiotemporal parameters from skeleton sequences. However, two critical problems exist: (1) previous skeleton sequences were created by connecting different joints with a static order; (2) earlier methods were not efficient enough to focus on valuable joints. Specifically, this study aimed to (1) demonstrate the ability of convolutional neural networks to learn spatiotemporal parameters of skeleton sequences from different frames of human action, and (2) to combine the process of all frames created by different human actions and fit in the spatial structure information necessary for action recognition, using multi-task learning networks (MTLNs). The results were significantly improved compared with existing models by executing the proposed model on an NTU RGB+D dataset, an SYSU dataset, and an SBU Kinetic Interaction dataset. We further implemented our model on noisy expected poses from subgroups of the Kinetics dataset and the UCF101 dataset. The experimental results also showed significant improvement using our proposed model.
format	article
author	Faisal Mehmood Enqing Chen Muhammad Azeem Akbar Abeer Abdulaziz Alsanad
author_facet	Faisal Mehmood Enqing Chen Muhammad Azeem Akbar Abeer Abdulaziz Alsanad
author_sort	Faisal Mehmood
title	Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
title_short	Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
title_full	Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
title_fullStr	Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
title_full_unstemmed	Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
title_sort	human action recognition of spatiotemporal parameters for skeleton sequences using mtln feature learning framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ef9865d76f654905b711d50b6b7c2dd9
work_keys_str_mv	AT faisalmehmood humanactionrecognitionofspatiotemporalparametersforskeletonsequencesusingmtlnfeaturelearningframework AT enqingchen humanactionrecognitionofspatiotemporalparametersforskeletonsequencesusingmtlnfeaturelearningframework AT muhammadazeemakbar humanactionrecognitionofspatiotemporalparametersforskeletonsequencesusingmtlnfeaturelearningframework AT abeerabdulazizalsanad humanactionrecognitionofspatiotemporalparametersforskeletonsequencesusingmtlnfeaturelearningframework
_version_	1718434122662150144

Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework

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