Soccer Formation Classification Based on Fisher Weight Map and Gaussian Mixture Models

摘要

This paper proposes a method that analyzes player formations in order to classify kick and throw-in events in soccer matches. Formations are described in terms of local head counts and mean velocities, which are converted into canonical variates using a Fisher weight map in order to select effective variates for discriminating between events. The map is acquired by supervised learning. The distribution of the variates for each event class is modeled by Gaussian mixtures in order to handle its multimodality in canonical space. Our experiments showed that the Fisher weight map extracted semantically explicable variates related to such situations as players at corners and left/right separation. Our experiments also showed that characteristically formed events, such as kick-offs and corner-kicks, were successfully classified by the Gaussian mixture models. The effect of spatial nonlinearity and fuzziness of local head counts are also evaluated.