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 |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10856153/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |