Article: Deep learning-based football training movement analysis and penalty feedback research Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.4/5/6 pp.276 - 289 Abstract: Recent advances in video understanding have enabled referee assistance in football, but reliance on single views or costly VAR systems limits their use in training and lower-tier contexts. In this work, we first propose a multi-view deep learning system for analysing football training movements and generating automated penalty feedback. The system processes four-view video sequences through video encoders, followed by a novel cross-view attention fusion module (CAFM) that adaptively integrates features from different viewpoints. Finally, to address view inconsistency and class imbalance in real-world data, we introduce a view completion strategy with augmentation and apply a class-balanced loss for classification. Experiments conducted on the SoccerNet-MVFoul dataset demonstrate that our method achieves 60.67% accuracy and 46.30% balanced accuracy in foul action classification. For foul severity classification, our approach reaches 53.63% accuracy and 54.14% balanced accuracy. Visualisation of attention weights confirms that the model successfully identifies the most informative viewpoints for each foul instance. These results show that the proposed system is effective and interpretable, offering a promising direction for referee assistance in non-professional football scenarios such as youth academies, amateur clubs, and grassroots training sessions. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-based football training movement analysis and penalty feedback research

Authors: Fubin Dai; Shuai Li; Zhigang Li

Addresses: Qujing University of Medicine & Health Sciences, Qujing – 655100, Yunnan, China ' Qujing University of Medicine & Health Sciences, Qujing – 655100, Yunnan, China ' Jilin Sport University, Changchun – 130031, Jilin, China

Abstract: Recent advances in video understanding have enabled referee assistance in football, but reliance on single views or costly VAR systems limits their use in training and lower-tier contexts. In this work, we first propose a multi-view deep learning system for analysing football training movements and generating automated penalty feedback. The system processes four-view video sequences through video encoders, followed by a novel cross-view attention fusion module (CAFM) that adaptively integrates features from different viewpoints. Finally, to address view inconsistency and class imbalance in real-world data, we introduce a view completion strategy with augmentation and apply a class-balanced loss for classification. Experiments conducted on the SoccerNet-MVFoul dataset demonstrate that our method achieves 60.67% accuracy and 46.30% balanced accuracy in foul action classification. For foul severity classification, our approach reaches 53.63% accuracy and 54.14% balanced accuracy. Visualisation of attention weights confirms that the model successfully identifies the most informative viewpoints for each foul instance. These results show that the proposed system is effective and interpretable, offering a promising direction for referee assistance in non-professional football scenarios such as youth academies, amateur clubs, and grassroots training sessions.

Keywords: computer vision; football; video assistant referee; VAR; video classification.

DOI: 10.1504/IJBIDM.2026.155236

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.276 - 289

Received: 01 Jun 2025
Accepted: 13 Jan 2026
Published online: 29 Jul 2026 *

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Title: Deep learning-based football training movement analysis and penalty feedback research

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