A Template-Based Multi-Player Action Recognition of the Basketball Game

In this paper we present a method for fully automatic trajectory based analysis of basketball game in the form of large and small scale modelling of the game. The large-scale game model is obtained by dividing the game into several game phases. Every game phase is then individually modelled using mixture of Gaussian distributions. The Expectation-Maximization algorithm is used to determine the parameters of the Gaussian distributions. On the other hand, the small-scale modelling of the game deals with specific basketball actions which can be defined in the form of action templates that are used by the basketball experts to pass their instructions to the players. For the recognition purposes we define the basic game elements which are the building blocks of the more complex game actions. These elements are then used to semantically describe the observed basketball actions and the templates. To establish if the observed action corresponds to the template, the similarity of descriptions is calculated using Levenstein distance measure. Experiments show that the proposed method could become a powerful tool for the recognition of various basketball actions.