Abstract
Cameras in sports continuously track athletes, recognize their activities, and monitor performance. This capability is ensured by sophisticated computer vision systems with machine learning algorithms and massive computation power. Combat sports are rather challenging because punches happen rather quickly. This paper provides comprehensive research on approaches to measuring the performance of athletes in combat sports. We use RGB cameras to measure athletes’ activity from a distance without interfering with their equipment, in contrast to the approach which uses wearable sensors. The aim of this paper is to provide a solution to classify punches in Olympic boxing based on static RGB cameras opposite the boxing ring. The proposed solution classifies three types of punches and the best classifier obtained sequentially 94%, 84%, and 81% of the F1 score for them. Finally, we measured the impact of the data augmentation process on classification performance and provide future works.
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Stefański, P., Jach, T., Kozak, J. (2023). Classification of Punches in Olympic Boxing Using Static RGB Cameras. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_41
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