Towards the automatic classification of football game events for the production of metadata based on the stadium main camera

Abstract

Being able to automatically personalize football content according to each viewer's preferences is the future of the broadcasting world. One of the steps is to be able to classify the current game event in a football video stream. During the game, the main camera is filming a panoramic view of the football game events. This master's thesis introduces a three stages framework where global features such as the field, the players and the lines are extracted from the video stream of the main camera and processed to compute second stage features that are used inside of a decision mechanism to predict the current game event. Computer vision and machine learning techniques such as deep learning networks and semantic segmentation are used to extract the main features. From these features, some second stage features such as the extraction of the main circle or the mean position of the players inside of the field are computed and handed out to a decision tree that classifies the current game event. A playout application developed in this master's thesis allows the user to visualize some of the features computed by the algorithms and the game event predicted. The performances of the features extraction found in this work are impressive, especially for the line extraction problem for which we have a global accuracy of 95% at the pixel level. While the interpretation of game events remains challenging and unsolved, we managed, with our framework, to achieve an accuracy of more than 90% for the classification between attack, defense and middle game.