Estimation of player trajectories from context in football games using autoencoders

Manon DEPRETTE

Abstract

ChyronHego tracks a large number of football games. Occasionally there are errors in the tracked positions of the ball or the players. This thesis aims to investigate to what extent vanilla AutoEncoders, Variational AutoEncoders and Conditional Variational AutoEncoders can recognise patterns in the data and thus be used to predict missing data for individual agents' trajectories in an adversarial multi-agent situation such as a football game. Furthermore, we also implement a multi-agent role alignment technique to order the outfield players in the dataset and use their identity, learnt unsupervised, in the predictions. We find out that in cases where the model cannot sufficiently rely on the individual agent's trajectory information, it efficiently uses the context, i.e. the other agents behaviour, to make more accurate predictions of the missing data. However, the identities of the players do not seem to improve the predictions of the models.