FootApp: an AI-Powered System for Football Match Annotation

TL;DR

FootApp is an AI-powered football match annotation system that combines user interaction, inertial sensor data, and machine learning to improve annotation accuracy and capture team dynamics beyond traditional camera-based methods.

Contribution

The paper introduces FootApp, a novel system integrating AI, inertial sensors, and a mixed interface for more accurate and comprehensive football match annotation.

Findings

Effective in real-world scenarios

Improves annotation accuracy

Captures team dynamics beyond visual data

Abstract

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players' and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics, and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario.