Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human beh...

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Autor principal:	Chengkun Lu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/91918a32136e490eb3250c399ae36a12
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id	oai:doaj.org-article:91918a32136e490eb3250c399ae36a12
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spelling	oai:doaj.org-article:91918a32136e490eb3250c399ae36a122021-11-22T01:10:23ZMultifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning1875-905X10.1155/2021/2199930https://doaj.org/article/91918a32136e490eb3250c399ae36a122021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2199930https://doaj.org/toc/1875-905XThrough the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.Chengkun LuHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Chengkun Lu Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning
description	Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.
format	article
author	Chengkun Lu
author_facet	Chengkun Lu
author_sort	Chengkun Lu
title	Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning
title_short	Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning
title_full	Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning
title_fullStr	Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning
title_full_unstemmed	Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning
title_sort	multifeature fusion human motion behavior recognition algorithm using deep reinforcement learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/91918a32136e490eb3250c399ae36a12
work_keys_str_mv	AT chengkunlu multifeaturefusionhumanmotionbehaviorrecognitionalgorithmusingdeepreinforcementlearning
_version_	1718418354913411072

Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

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