Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement
The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognitio...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8889008 |
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| author | Wenhua Fan Hyun Joo Min |
| author_facet | Wenhua Fan Hyun Joo Min |
| author_sort | Wenhua Fan |
| collection | DOAJ |
| description | The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics of aerobics, this paper builds a convolutional neural network model. The model is improved on the basis of the traditional model and unifies the process of aerobics 3D visual image segmentation, target feature extraction, and target recognition. The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3D visual image test set for recognition and comparison. We improve the accuracy of network recognition by adjusting the configuration parameters in the network model. The experimental results show that compared with other simple models, the model based on the improved AdaBoost algorithm can improve the final result significantly when the accuracy of each model is average. Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model. |
| format | Article |
| id | doaj-art-a02238f34eca42429ad715767d20af84 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a02238f34eca42429ad715767d20af842025-02-03T01:00:39ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88890088889008Accurate Recognition and Simulation of 3D Visual Image of Aerobics MovementWenhua Fan0Hyun Joo Min1Sports Institute, Jiaying University, Meizhou 514015, ChinaSports Institute, Korea Gangneung-Wonju National University, Gangneung 25457, Republic of KoreaThe structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics of aerobics, this paper builds a convolutional neural network model. The model is improved on the basis of the traditional model and unifies the process of aerobics 3D visual image segmentation, target feature extraction, and target recognition. The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3D visual image test set for recognition and comparison. We improve the accuracy of network recognition by adjusting the configuration parameters in the network model. The experimental results show that compared with other simple models, the model based on the improved AdaBoost algorithm can improve the final result significantly when the accuracy of each model is average. Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model.http://dx.doi.org/10.1155/2020/8889008 |
| spellingShingle | Wenhua Fan Hyun Joo Min Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement Complexity |
| title | Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement |
| title_full | Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement |
| title_fullStr | Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement |
| title_full_unstemmed | Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement |
| title_short | Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement |
| title_sort | accurate recognition and simulation of 3d visual image of aerobics movement |
| url | http://dx.doi.org/10.1155/2020/8889008 |
| work_keys_str_mv | AT wenhuafan accuraterecognitionandsimulationof3dvisualimageofaerobicsmovement AT hyunjoomin accuraterecognitionandsimulationof3dvisualimageofaerobicsmovement |