Using YOLO athlete cropping and Depth Anything V2 depth maps, a CNN classifies calisthenics skills with 0.837 accuracy, slightly above a 0.815 OpenPose skeleton baseline.
Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action
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abstract
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.
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Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
Using YOLO athlete cropping and Depth Anything V2 depth maps, a CNN classifies calisthenics skills with 0.837 accuracy, slightly above a 0.815 OpenPose skeleton baseline.