Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization
This work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video image...
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| Language: | English |
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Elsevier
2024-10-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024148967 |
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| author | Chuanbao He Min Zhang |
| author_facet | Chuanbao He Min Zhang |
| author_sort | Chuanbao He |
| collection | DOAJ |
| description | This work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video images are input into a convolutional neural network frame by frame. By adding an attention mechanism, it helps identify the badminton movement trajectory. Finally, to address the detection challenge of flying badminton as a small target in video streams, the deep learning one-stage detection network, Tiny YOLOv2, is improved from both the loss function and network structure perspectives. Moreover, it is combined with the Unscented Kalman Filter algorithm to predict the trajectory of badminton movement. Simulation results show that the improved algorithm performs excellently in tracking and predicting badminton trajectories compared with the existing algorithms. The average accuracy of the proposed method for tracking badminton trajectories is 91.40 %, and the recall rate is 84.60 %. The average precision, recall, and frame rate of the measured trajectories in four simple and complex scenarios of badminton flight video streams are 96.7 %, 95.7 %, and 29.2 frames/second, respectively. They are all superior to other classic algorithms. It is evident that the proposed method can provide powerful support for badminton trajectory recognition and help improve the accuracy of badminton movement recognition. |
| format | Article |
| id | doaj-art-a3d3ec3a728f44bbb6610e6cf1201066 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-a3d3ec3a728f44bbb6610e6cf12010662024-11-12T05:19:21ZengElsevierHeliyon2405-84402024-10-011020e38865Deep learning neural network-assisted badminton movement recognition and physical fitness training optimizationChuanbao He0Min Zhang1Department of Physical Education, Tianjin Sino-German University of Applied Sciences, Tianjin, 300350, China; Corresponding author.Graduate School, Metharath University, Bangkok, 10400, ThailandThis work aims to solve the problem of low accuracy in recognizing the trajectory of badminton movement. This work focuses on the visual system in badminton robots and conducts side detection and tracking of flying badminton in two-dimensional image plane video streams. Then, the cropped video images are input into a convolutional neural network frame by frame. By adding an attention mechanism, it helps identify the badminton movement trajectory. Finally, to address the detection challenge of flying badminton as a small target in video streams, the deep learning one-stage detection network, Tiny YOLOv2, is improved from both the loss function and network structure perspectives. Moreover, it is combined with the Unscented Kalman Filter algorithm to predict the trajectory of badminton movement. Simulation results show that the improved algorithm performs excellently in tracking and predicting badminton trajectories compared with the existing algorithms. The average accuracy of the proposed method for tracking badminton trajectories is 91.40 %, and the recall rate is 84.60 %. The average precision, recall, and frame rate of the measured trajectories in four simple and complex scenarios of badminton flight video streams are 96.7 %, 95.7 %, and 29.2 frames/second, respectively. They are all superior to other classic algorithms. It is evident that the proposed method can provide powerful support for badminton trajectory recognition and help improve the accuracy of badminton movement recognition.http://www.sciencedirect.com/science/article/pii/S2405844024148967Deep learning neural networksBadminton movementTrajectory recognition and classificationMovement trajectory prediction |
| spellingShingle | Chuanbao He Min Zhang Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization Heliyon Deep learning neural networks Badminton movement Trajectory recognition and classification Movement trajectory prediction |
| title | Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization |
| title_full | Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization |
| title_fullStr | Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization |
| title_full_unstemmed | Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization |
| title_short | Deep learning neural network-assisted badminton movement recognition and physical fitness training optimization |
| title_sort | deep learning neural network assisted badminton movement recognition and physical fitness training optimization |
| topic | Deep learning neural networks Badminton movement Trajectory recognition and classification Movement trajectory prediction |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024148967 |
| work_keys_str_mv | AT chuanbaohe deeplearningneuralnetworkassistedbadmintonmovementrecognitionandphysicalfitnesstrainingoptimization AT minzhang deeplearningneuralnetworkassistedbadmintonmovementrecognitionandphysicalfitnesstrainingoptimization |