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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2312.15236 |
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| _version_ | 1866915466312679424 |
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| author | Torón-Artiles, Javier Hernández-Sosa, Daniel Santana, Oliverio J. Lorenzo-Navarro, Javier Freire-Obregón, David |
| author_facet | Torón-Artiles, Javier Hernández-Sosa, Daniel Santana, Oliverio J. Lorenzo-Navarro, Javier Freire-Obregón, David |
| contents | This research addresses whether the ball's direction after a soccer free-kick can be accurately predicted solely by observing the shooter's kicking technique. To investigate this, we meticulously curated a dataset of soccer players executing free kicks and conducted manual temporal segmentation to identify the moment of the kick precisely. Our approach involves utilizing neural networks to develop a model that integrates Human Action Recognition (HAR) embeddings with contextual information, predicting the ball-on-goal position (BoGP) based on two temporal states: the kicker's run-up and the instant of the kick. The study encompasses a performance evaluation for eleven distinct HAR backbones, shedding light on their effectiveness in BoGP estimation during free-kick situations. An extra tabular metadata input is introduced, leading to an interesting model enhancement without introducing bias. The promising results reveal 69.1% accuracy when considering two primary BoGP classes: right and left. This underscores the model's proficiency in predicting the ball's destination towards the goal with high accuracy, offering promising implications for understanding free-kick dynamics in soccer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15236 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action Torón-Artiles, Javier Hernández-Sosa, Daniel Santana, Oliverio J. Lorenzo-Navarro, Javier Freire-Obregón, David Computer Vision and Pattern Recognition This research addresses whether the ball's direction after a soccer free-kick can be accurately predicted solely by observing the shooter's kicking technique. To investigate this, we meticulously curated a dataset of soccer players executing free kicks and conducted manual temporal segmentation to identify the moment of the kick precisely. Our approach involves utilizing neural networks to develop a model that integrates Human Action Recognition (HAR) embeddings with contextual information, predicting the ball-on-goal position (BoGP) based on two temporal states: the kicker's run-up and the instant of the kick. The study encompasses a performance evaluation for eleven distinct HAR backbones, shedding light on their effectiveness in BoGP estimation during free-kick situations. An extra tabular metadata input is introduced, leading to an interesting model enhancement without introducing bias. The promising results reveal 69.1% accuracy when considering two primary BoGP classes: right and left. This underscores the model's proficiency in predicting the ball's destination towards the goal with high accuracy, offering promising implications for understanding free-kick dynamics in soccer. |
| title | Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.15236 |