Predicting viewer-perceived activity/dominance in soccer games with stick-breaking HMM using data from a fixed set of cameras

Abstract

We attempted to predict activity/dominance for soccer games, where activity is defined as the degree of activity of the game as perceived by the viewer, whereas dominance is the degree at which the viewer perceives a particular team to dominate over the other team. Such activity/dominance information would help a layman viewer understand the game. It would also enable construction of an automatic digest creation system that extracts scenes having high activity/dominance. There are two facets of this study: 1. The main part of the underlying prediction model consists of a Stick-Breaking Hidden Markov Model, where the data automatically estimates the number of states of the Markov process behind the data. 2. The data used in this paper is vector time-series data consisting of player, referee, and ball positions, together with team information, acquired by a set of fixed cameras. The problem was approached with a Bayesian framework where learning and prediction were implemented by three different methods: Markov Chain Monte Carlo, Expectation Maximization, and Variational Bayes. The proposed method was tested using a dataset consisting of 10 professional soccer games and was compared against standard regression methods.