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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.26088 |
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| _version_ | 1866909816643911680 |
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| author | Ranasinghe, Pasindu Ranasinghe, Pamudu |
| author_facet | Ranasinghe, Pasindu Ranasinghe, Pamudu |
| contents | Penalty kicks often decide championships, yet goalkeepers must anticipate the kicker's intent from subtle biomechanical cues within a very short time window. This study introduces a real-time, multi-modal deep learning framework to predict the direction of a penalty kick (left, middle, or right) before ball contact. The model uses a dual-branch architecture: a MobileNetV2-based CNN extracts spatial features from RGB frames, while 2D keypoints are processed by an LSTM network with attention mechanisms. Pose-derived keypoints further guide visual focus toward task-relevant regions. A distance-based thresholding method segments input sequences immediately before ball contact, ensuring consistent input across diverse footage. A custom dataset of 755 penalty kick events was created from real match videos, with frame-level annotations for object detection, shooter keypoints, and final ball placement. The model achieved 89% accuracy on a held-out test set, outperforming visual-only and pose-only baselines by 14-22%. With an inference time of 22 milliseconds, the lightweight and interpretable design makes it suitable for goalkeeper training, tactical analysis, and real-time game analytics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26088 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Penalty Kick Direction Using Multi-Modal Deep Learning with Pose-Guided Attention Ranasinghe, Pasindu Ranasinghe, Pamudu Computer Vision and Pattern Recognition Penalty kicks often decide championships, yet goalkeepers must anticipate the kicker's intent from subtle biomechanical cues within a very short time window. This study introduces a real-time, multi-modal deep learning framework to predict the direction of a penalty kick (left, middle, or right) before ball contact. The model uses a dual-branch architecture: a MobileNetV2-based CNN extracts spatial features from RGB frames, while 2D keypoints are processed by an LSTM network with attention mechanisms. Pose-derived keypoints further guide visual focus toward task-relevant regions. A distance-based thresholding method segments input sequences immediately before ball contact, ensuring consistent input across diverse footage. A custom dataset of 755 penalty kick events was created from real match videos, with frame-level annotations for object detection, shooter keypoints, and final ball placement. The model achieved 89% accuracy on a held-out test set, outperforming visual-only and pose-only baselines by 14-22%. With an inference time of 22 milliseconds, the lightweight and interpretable design makes it suitable for goalkeeper training, tactical analysis, and real-time game analytics. |
| title | Predicting Penalty Kick Direction Using Multi-Modal Deep Learning with Pose-Guided Attention |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.26088 |