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Vision-based Assessment of Instructional Content on Golf Performance

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Published:15 July 2022Publication History

ABSTRACT

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.

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  1. Vision-based Assessment of Instructional Content on Golf Performance

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    • Published in

      cover image ACM Other conferences
      IPMV '22: Proceedings of the 4th International Conference on Image Processing and Machine Vision
      March 2022
      121 pages
      ISBN:9781450395823
      DOI:10.1145/3529446

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      Publication History

      • Published: 15 July 2022

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