Dribbling posture estimation using a human skeleton model

We aim to evaluate dribbling movements in soccer by analyzing the movement of both the player and the ball. For this purpose, we have extended the existing skeletal estimation model, OpenPose, and propose a new method to recognize the posture of human skeletons holding the ball, which we have named the ”Dribbling Player Model.” A distinctive feature of this model is that the ball is incorporated as part of the human skeleton. To train this model, we handcrafted a video database of dribbling actions. We annotated videos of players who were dribbling with a skeleton that included the ball, and videos of players who were not dribbling were annotated without the ball. Our experiments have confirmed the success of estimating the dribbling postures of players who are dribbling.