Generating Deeply-Engineered Technical Features for Basketball Video Understanding
Investigating video-guided basketball movement understanding is essential for enhancing sports coaching. Integrating basketball videos with human-computer interaction (HCI) algorithms significantly improves training efficiency. In this paper, we propose a novel method for basketball player motion re...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856153/ |
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| author | Shaohua Fang Guifeng Wang Yongbin Li Yue Yu Jun Li |
| author_facet | Shaohua Fang Guifeng Wang Yongbin Li Yue Yu Jun Li |
| author_sort | Shaohua Fang |
| collection | DOAJ |
| description | Investigating video-guided basketball movement understanding is essential for enhancing sports coaching. Integrating basketball videos with human-computer interaction (HCI) algorithms significantly improves training efficiency. In this paper, we propose a novel method for basketball player motion recognition and prediction. We engineer the technical features of gameplay through video analysis and introduce a behavioral analysis method using a multi-layer learning architecture. Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. Experimental validation on a comprehensive basketball video dataset demonstrates the effectiveness of our method in accurately identifying player movements and analyzing behaviors. |
| format | Article |
| id | doaj-art-b5e72e9f67664c108968ba409d26eba6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b5e72e9f67664c108968ba409d26eba62025-02-04T00:00:49ZengIEEEIEEE Access2169-35362025-01-0113206672067710.1109/ACCESS.2025.353580810856153Generating Deeply-Engineered Technical Features for Basketball Video UnderstandingShaohua Fang0Guifeng Wang1Yongbin Li2Yue Yu3Jun Li4https://orcid.org/0009-0008-9353-2562Aeronautical Engineering College, Jinhua University of Vocational Technology, Jinhua, Zhejiang, ChinaAeronautical Engineering College, Jinhua University of Vocational Technology, Jinhua, Zhejiang, ChinaAeronautical Engineering College, Jinhua University of Vocational Technology, Jinhua, Zhejiang, ChinaIntelligent Manufacturing College, Jinhua University of Vocational Technology, Jinhua, Zhejiang, ChinaCollege of Computer Sciences, Anhui University, Hefei, ChinaInvestigating video-guided basketball movement understanding is essential for enhancing sports coaching. Integrating basketball videos with human-computer interaction (HCI) algorithms significantly improves training efficiency. In this paper, we propose a novel method for basketball player motion recognition and prediction. We engineer the technical features of gameplay through video analysis and introduce a behavioral analysis method using a multi-layer learning architecture. Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. Experimental validation on a comprehensive basketball video dataset demonstrates the effectiveness of our method in accurately identifying player movements and analyzing behaviors.https://ieeexplore.ieee.org/document/10856153/Basketballmotion trackinghuman-computer interactionvideo analysisCNNsRNNs |
| spellingShingle | Shaohua Fang Guifeng Wang Yongbin Li Yue Yu Jun Li Generating Deeply-Engineered Technical Features for Basketball Video Understanding IEEE Access Basketball motion tracking human-computer interaction video analysis CNNs RNNs |
| title | Generating Deeply-Engineered Technical Features for Basketball Video Understanding |
| title_full | Generating Deeply-Engineered Technical Features for Basketball Video Understanding |
| title_fullStr | Generating Deeply-Engineered Technical Features for Basketball Video Understanding |
| title_full_unstemmed | Generating Deeply-Engineered Technical Features for Basketball Video Understanding |
| title_short | Generating Deeply-Engineered Technical Features for Basketball Video Understanding |
| title_sort | generating deeply engineered technical features for basketball video understanding |
| topic | Basketball motion tracking human-computer interaction video analysis CNNs RNNs |
| url | https://ieeexplore.ieee.org/document/10856153/ |
| work_keys_str_mv | AT shaohuafang generatingdeeplyengineeredtechnicalfeaturesforbasketballvideounderstanding AT guifengwang generatingdeeplyengineeredtechnicalfeaturesforbasketballvideounderstanding AT yongbinli generatingdeeplyengineeredtechnicalfeaturesforbasketballvideounderstanding AT yueyu generatingdeeplyengineeredtechnicalfeaturesforbasketballvideounderstanding AT junli generatingdeeplyengineeredtechnicalfeaturesforbasketballvideounderstanding |