Vision-based Assessment of Instructional Content on Golf Performance

This paper focuses on the detection and tracking of a golf ball from video input, as a part of a study aiming to evaluate the influence of instructional content delivered through Acceptance and Commitment Training (ACT) on sports performance. As part of this experiment, the participants were putting on a small green region, and they recorded themselves performing the swing. Using our generated dataset, we propose an automated solution that involves only vision processing to detect and track the golf ball, and to estimate metrics such as velocity, shot angle, and whether the golf ball made the hole. We use color detection, background subtraction, contour detection, homography transformations, and other techniques to detect and track the golf ball. Our approach was extensively tested on a large dataset with annotations, with good results.