Analysis of soccer matches with clustering of player trajectories and Mutual Information based metric

Abstract

The main goal of this project is to extract high level semantic cues from soccer match video sequences, using machine learning and computer vision techniques.

  • During the first year, I explored the state-of-the-art techniques that could be used to extract the player trajectories given the videos of a match recorded with several static camera.

  • During the second year, I focused on the player trajectories clustering problem. I particularly focused on designing a reliable and efficient metric to establish correspondences between trajectories.

Introduction

State-of-the art methods on soccer match analysis use extra high level data and annotations (such as whoscored, transfertmark) which require human annotation and so preprocessing