Structure analysis of soccer video with hidden Markov models

Abstract

In this paper, we present algorithms for parsing the structure of produced soccer programs. The problem is important in the context of a personalized video streaming and browsing system. While prior work focuses on the detection of special events such as goals or corner kicks, this paper is concerned with generic structural elements of the game. We begin by defining two mutually exclusive states of the game, play and break based on the rules of soccer. We select a domain-tuned feature set, dominant color ratio and motion intensity, based on the special syntax and content characteristics of soccer videos. Each state of the game has a stochastic structure that is modeled with a set of hidden Markov models. Finally, standard dynamic programming techniques are used to obtain the maximum likelihood segmentation of the game into the two states. The system works well, with 83.5% classification accuracy and good boundary timing from extensive tests over diverse data sets.