Priors for the Ball Position in Football Match using

Due to the growth of media services in football broadcasting, a Swedish company called TRACAB is modelling the reconstruction of football matches in 3-D. TRACAB has developed a system to track players’ positions for the whole match using cameras installed in each stadium. However, this system is less reliable when applied to tracking the football. Therefore this Master Thesis focuses on estimating the ball’s position from the players’ trajectories. In particular we investigate exploiting the contextual information to localize the ball. The estimation of the position of the ball is calculated using different supervised learning methods from feature vectors which are built with players’ positions. At first, exact estimated coordinates are researched in general situations where no priors are given. After looking at the results, this work selects regions which are more suitable to find the ball. Another scenario is investigated where it is assumed that we know which player has the ball. Then we try to predict to which region he will pass the ball to. Consequently, TRACAB could find the ball efficiently on the pitch. Results are reported from data provided by TRACAB. These data are the coordinates of the ball and players’ positions of a particular whole match. Histograms and percentages of success illustrate results and error aproximations in each case.