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Spatiotemporal key point detection in golf swing sequences via Hybrid CNN-TCN architecture and regression-based refinement

human-computer-interaction
artificial-intelligence
computer-vision
data-mining-and-machine-learning
multimedia

Abstract

Finding structural keypoints is an important part of sports analytics, especially for activities that require accuracy, like golf, where detailed motion analysis helps improve performance. This study shows how to use deep learning to find very small joint positions in the GolfDB dataset. We suggest a unique method that uses both convolutional and temporal modelling together with a hybrid heatmap-regression refinement process. Using Convolutional Neural Networks (CNNs) and a Temporal Convolutional Network (TCN), our method suggests a spatial feature extraction network that models the swing's sequential dynamics. We use a hybrid heatmap-regression refinement technique to improve the accuracy and temporal consistency of critical point localisation even more. The novel technique is more spatially accurate and temporally consistent than previous systems. It also enables trainers and biomechanical analysts to draw real-time inferences. Numerous experiments show that our solution outperforms traditional key point detection algorithms because it addresses issues that are unique to quick, intricate golf movements.
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