Extraction of Positional Player Data from Broadcast Soccer Videos

TL;DR

This paper presents a fully automated pipeline for extracting player positional data from broadcast soccer videos, integrating multiple modules and evaluating their combined accuracy with novel metrics.

Contribution

It introduces an end-to-end system that automates soccer player position extraction from broadcast videos, including new evaluation metrics for accuracy assessment.

Findings

The integrated pipeline achieves accurate positional data extraction.

Novel evaluation metrics effectively measure system performance.

Error analysis highlights module interdependencies.

Abstract

Computer-aided support and analysis are becoming increasingly important in the modern world of sports. The scouting of potential prospective players, performance as well as match analysis, and the monitoring of training programs rely more and more on data-driven technologies to ensure success. Therefore, many approaches require large amounts of data, which are, however, not easy to obtain in general. In this paper, we propose a pipeline for the fully-automated extraction of positional data from broadcast video recordings of soccer matches. In contrast to previous work, the system integrates all necessary sub-tasks like sports field registration, player detection, or team assignment that are crucial for player position estimation. The quality of the modules and the entire system is interdependent. A comprehensive experimental evaluation is presented for the individual modules as well as the entire pipeline to identify the influence of errors to subsequent modules and the overall result. In this context, we propose novel evaluation metrics to compare the output with ground-truth positional data.