Basketball motion recognition and tracking method based on improved convolutional neural network
To improve the accuracy of basketball motion analysis, this study proposes a basketball motion recognition and tracking method based on an improved convolutional neural network. This method combines an intelligent sensor system with an improved dual-mode convolutional neural network to identify bask...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925000900 |
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| _version_ | 1850278342669893632 |
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| author | Gong Yan |
| author_facet | Gong Yan |
| author_sort | Gong Yan |
| collection | DOAJ |
| description | To improve the accuracy of basketball motion analysis, this study proposes a basketball motion recognition and tracking method based on an improved convolutional neural network. This method combines an intelligent sensor system with an improved dual-mode convolutional neural network to identify basketball motion steps; A tracking method based on the Northeast sky coordinate system was proposed to depict the motion trajectory of basketball players. The experimental results show that the average recognition accuracy of the improved convolutional neural network model is 99.3 %, which is superior to K-nearest neighbors and other models. This model structure can better capture the complexity and diversity of basketball footwork, improve recognition accuracy, and enhance generalization ability, while still maintaining high recognition accuracy in the face of new movements. The average error of linear trajectory tracking is 4.3 %, while the average errors of curved trajectory tracking in the X, Y, and Z directions are 4.1 %, 5.9 %, and 6.1 %, respectively. Research has shown that this method provides an effective approach for basketball analysis and training, which helps to improve the competitive level of basketball players. |
| format | Article |
| id | doaj-art-a4b0f49ecccc4b3b9a6f392887b89f46 |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-a4b0f49ecccc4b3b9a6f392887b89f462025-08-20T01:49:32ZengElsevierSystems and Soft Computing2772-94192025-12-01720027210.1016/j.sasc.2025.200272Basketball motion recognition and tracking method based on improved convolutional neural networkGong Yan0Corresponding author.; School of Physical Education and Health, Jiangxi Science & Technology Normal University, Nanchang 330000, ChinaTo improve the accuracy of basketball motion analysis, this study proposes a basketball motion recognition and tracking method based on an improved convolutional neural network. This method combines an intelligent sensor system with an improved dual-mode convolutional neural network to identify basketball motion steps; A tracking method based on the Northeast sky coordinate system was proposed to depict the motion trajectory of basketball players. The experimental results show that the average recognition accuracy of the improved convolutional neural network model is 99.3 %, which is superior to K-nearest neighbors and other models. This model structure can better capture the complexity and diversity of basketball footwork, improve recognition accuracy, and enhance generalization ability, while still maintaining high recognition accuracy in the face of new movements. The average error of linear trajectory tracking is 4.3 %, while the average errors of curved trajectory tracking in the X, Y, and Z directions are 4.1 %, 5.9 %, and 6.1 %, respectively. Research has shown that this method provides an effective approach for basketball analysis and training, which helps to improve the competitive level of basketball players.http://www.sciencedirect.com/science/article/pii/S2772941925000900CNNDMCNNSensorMotion stepTrajectory tracking |
| spellingShingle | Gong Yan Basketball motion recognition and tracking method based on improved convolutional neural network Systems and Soft Computing CNN DMCNN Sensor Motion step Trajectory tracking |
| title | Basketball motion recognition and tracking method based on improved convolutional neural network |
| title_full | Basketball motion recognition and tracking method based on improved convolutional neural network |
| title_fullStr | Basketball motion recognition and tracking method based on improved convolutional neural network |
| title_full_unstemmed | Basketball motion recognition and tracking method based on improved convolutional neural network |
| title_short | Basketball motion recognition and tracking method based on improved convolutional neural network |
| title_sort | basketball motion recognition and tracking method based on improved convolutional neural network |
| topic | CNN DMCNN Sensor Motion step Trajectory tracking |
| url | http://www.sciencedirect.com/science/article/pii/S2772941925000900 |
| work_keys_str_mv | AT gongyan basketballmotionrecognitionandtrackingmethodbasedonimprovedconvolutionalneuralnetwork |