Lei Genghua and  Zhang Long. Football Player Action Detection Algorithm Based on Gaussian Mixture Model. Dynamic Systems and Applications 29 (2020) No. 5, 2110 – 2116

https://doi.org/10.46719/dsa202029545

ABSTRACT.
The data generation and availability have increased significantly over the past few decades, generally because of its prominence, and because of mechanical advances. Gaussian blend model grouping speaks to a novel method to investigate and break down game execution information. In this article, we join head part investigation with the point of describing an expert soccer player, a model-based Gaussian grouping technique.To test the method of our model is to use 40 attributes of the FIFA series video game system in EA sports, corresponding to European players. Performance indicators with different clustering results show that they are significantly different compared to the four different roles of the team. Players use a gradient tree that is used to rank these attributes related to their role and their importance to enhance the model’s tags. We find it to be the essential variable in the profile of different clusters of players dribbling skills.

Keywords: association football, preprocessing, segmentation, Gaussian mixture clustering models, classification.