What are the primary methods used in recent papers to model player action processes or sequences in sports?
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Recent papers have proposed various methods to model player action processes or sequences in sports. These methods include utilizing machine vision for athlete action recognition , employing neural marked temporal point processes (MTPP) for continuous-time distribution modeling of actions in activity sequences , using generic low-level features and latent semantic analysis for unsupervised player modeling to reveal hidden game semantics , and constructing a sports player motion recognition model based on the maximum spanning tree algorithm combined with region growing and watershed segmentation algorithms . These approaches aim to enhance understanding of player actions, improve action recognition accuracy, predict activity lengths, recommend next actions, and generate end-to-end action sequences in sports scenarios.
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29 Aug 2022 | The primary method used is a piecewise linear supervised learning model analyzing player displacements to predict camera motion for real-time action tracking in sports. |
| The primary methods used are machine vision for capturing athlete actions through cameras and big data analysis for exploring historical training and competition data to enhance training interventions. | |
1 Citations | The primary methods used in recent papers to model player action processes in sports include the maximum spanning tree algorithm, region growing algorithm, simple surface fitting, morphological reconstruction, and optimized watershed segmentation. |
| Not addressed in the paper. | |
| Not addressed in the paper. |
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