Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network
This study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/22/3797 |
| _version_ | 1797465544303050752 |
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| author | Kun Zuo Xiaofeng Su |
| author_facet | Kun Zuo Xiaofeng Su |
| author_sort | Kun Zuo |
| collection | DOAJ |
| description | This study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method, the present method makes the recognition accuracy better and the display effect more intuitive. The flipped classroom teaching mode based on this algorithm is applied to the college sports basketball optional course to explore the influence of this teaching mode on the classroom teaching effect. Compared with the evaluation index of action recognition, the experimental results of various action recognition methods and datasets are compared and analyzed, and it is verified that the method has a good recognition effect. The values of Topi and Top5 of the proposed method are 42.21% and 88.77%, respectively, which are 10.61% and 35.09% higher than those of the Kinetics-skeleton dataset. However, compared with the NTU RGM dataset, the recognition rate of Topi is significantly reduced. Compared with the traditional single-action recognition method, this method has better recognition accuracy and a more intuitive display effect. The fusion method of human posture estimation and motion recognition provides a new idea for basketball auxiliary training. |
| first_indexed | 2024-03-09T18:22:58Z |
| format | Article |
| id | doaj.art-eaa61da2679c4c6f8887bed593421141 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T18:22:58Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-eaa61da2679c4c6f8887bed5934211412023-11-24T08:10:40ZengMDPI AGElectronics2079-92922022-11-011122379710.3390/electronics11223797Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural NetworkKun Zuo0Xiaofeng Su1Sports Department, Shanghai Polytechnic University, Shanghai 201209, ChinaDepartment of Physical Education, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, ChinaThis study proposes a 3D attitude estimation algorithm using the RMPE algorithm coupled with a deep neural network that combines human pose estimation and action recognition, which provides a new idea for basketball auxiliary training. Compared with the traditional single-action recognition method, the present method makes the recognition accuracy better and the display effect more intuitive. The flipped classroom teaching mode based on this algorithm is applied to the college sports basketball optional course to explore the influence of this teaching mode on the classroom teaching effect. Compared with the evaluation index of action recognition, the experimental results of various action recognition methods and datasets are compared and analyzed, and it is verified that the method has a good recognition effect. The values of Topi and Top5 of the proposed method are 42.21% and 88.77%, respectively, which are 10.61% and 35.09% higher than those of the Kinetics-skeleton dataset. However, compared with the NTU RGM dataset, the recognition rate of Topi is significantly reduced. Compared with the traditional single-action recognition method, this method has better recognition accuracy and a more intuitive display effect. The fusion method of human posture estimation and motion recognition provides a new idea for basketball auxiliary training.https://www.mdpi.com/2079-9292/11/22/3797neural deep learningteaching designcollege basketballmovement identificationflipped classroom |
| spellingShingle | Kun Zuo Xiaofeng Su Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network Electronics neural deep learning teaching design college basketball movement identification flipped classroom |
| title | Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network |
| title_full | Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network |
| title_fullStr | Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network |
| title_full_unstemmed | Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network |
| title_short | Three-Dimensional Action Recognition for Basketball Teaching Coupled with Deep Neural Network |
| title_sort | three dimensional action recognition for basketball teaching coupled with deep neural network |
| topic | neural deep learning teaching design college basketball movement identification flipped classroom |
| url | https://www.mdpi.com/2079-9292/11/22/3797 |
| work_keys_str_mv | AT kunzuo threedimensionalactionrecognitionforbasketballteachingcoupledwithdeepneuralnetwork AT xiaofengsu threedimensionalactionrecognitionforbasketballteachingcoupledwithdeepneuralnetwork |