Automatic Classification of Offensive Patterns for Soccer Game Highlights Using Neural Networks

Abstract

A method for the automatic classification of offensive patterns in soccer games has been developed using neural networks technique. Back-propagation (BP) neural network techniques have been applied to obtain data that define the positions of both a player and the ball on the ground. The offensive patterns have been formulated from the group formations and enable automatic indexing of the highlights of soccer games. Excepts from actual soccer games including some from the 1998 French World Cup yielded 297 video clips which were categorized into the following five types of pattern: Left-Running are 60, Right-Running 74, Center-Running 72, Corner-Kick 39 and Free-Kick 52. Examination of the results shows the following rates of satisfactory pattern recognition: Left-Running comes to 91.7%, Right-Running 100%, Center-Running 87.5%, Corner-Kick 97.4% and Free-Kick 75%.