Predicting Soccer Penalty Kick Direction Using Human Action Recognition
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913945726484480 |
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| author | Freire-Obregón, David Santana, Oliverio J. Lorenzo-Navarro, Javier Hernández-Sosa, Daniel Castrillón-Santana, Modesto |
| author_facet | Freire-Obregón, David Santana, Oliverio J. Lorenzo-Navarro, Javier Hernández-Sosa, Daniel Castrillón-Santana, Modesto |
| contents | Action anticipation has become a prominent topic in Human Action Recognition (HAR). However, its application to real-world sports scenarios remains limited by the availability of suitable annotated datasets. This work presents a novel dataset of manually annotated soccer penalty kicks to predict shot direction based on pre-kick player movements. We propose a deep learning classifier to benchmark this dataset that integrates HAR-based feature embeddings with contextual metadata. We evaluate twenty-two backbone models across seven architecture families (MViTv2, MViTv1, SlowFast, Slow, X3D, I3D, C2D), achieving up to 63.9% accuracy in predicting shot direction (left or right), outperforming the real goalkeepers' decisions. These results demonstrate the dataset's value for anticipatory action recognition and validate our model's potential as a generalizable approach for sports-based predictive tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12617 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Soccer Penalty Kick Direction Using Human Action Recognition Freire-Obregón, David Santana, Oliverio J. Lorenzo-Navarro, Javier Hernández-Sosa, Daniel Castrillón-Santana, Modesto Computer Vision and Pattern Recognition Action anticipation has become a prominent topic in Human Action Recognition (HAR). However, its application to real-world sports scenarios remains limited by the availability of suitable annotated datasets. This work presents a novel dataset of manually annotated soccer penalty kicks to predict shot direction based on pre-kick player movements. We propose a deep learning classifier to benchmark this dataset that integrates HAR-based feature embeddings with contextual metadata. We evaluate twenty-two backbone models across seven architecture families (MViTv2, MViTv1, SlowFast, Slow, X3D, I3D, C2D), achieving up to 63.9% accuracy in predicting shot direction (left or right), outperforming the real goalkeepers' decisions. These results demonstrate the dataset's value for anticipatory action recognition and validate our model's potential as a generalizable approach for sports-based predictive tasks. |
| title | Predicting Soccer Penalty Kick Direction Using Human Action Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.12617 |