Markerless tracking in sports using DeepLabCut: A methodological tutorial

Résumé fourni par la source

One of the major challenges in Motor Behavior research is translating findings from controlled environments to complex, real-world contexts such as team sports. Recent advances in machine learning, particularly deep learning, have enhanced the ability to track and analyse movement in dynamic settings. DeepLabCut, an open-source toolbox using deep neural networks, enables precise tracking of players and objects, offering valuable insights for performance analysis. This tutorial aims to provide practical guidance for implementing DeepLabCut and to evaluate its effectiveness in tracking both athletes and the ball during gameplay, thereby supporting its application in sports science research and professional practice.

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