The paper presents a group behavior representation and an application in the 2 vs. 2 basketball domain. Furthermore a set of forty features have been made from the raw information provided by the INEF12 Basketball Dataset. Moreover, from all these features we propose a selection using an algorithm to choose the best features to classify and predict the group behavior. The entire experimental test carried out with Hidden Markov Models algorithms could validate the proposed representation and features selection, in group behavior recognition and 2 vs. 2 basketball specific domain.
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