Towards Athlete Fatigue Assessment from Association Football Videos

2026-04-07

AI Summary

This work proposes a non-intrusive method for objectively assessing athlete fatigue using monocular broadcast videos of soccer matches, eliminating reliance on subjective self-reports or wearable sensors. By leveraging Game State Reconstruction to recover player trajectories in field coordinates and integrating a novel temporally consistent kinematic algorithm, the approach generates velocity and acceleration time-series signals to construct acceleration–speed (A–S) profiles that quantify fatigue-related metrics. The study demonstrates, for the first time, the feasibility of using broadcast footage for fatigue-oriented analysis and systematically evaluates the impact of real-world factors such as trajectory noise and calibration errors. Experiments on the SoccerNet-GSR benchmark confirm the method’s reliability and consistency across both short 30-second clips and full 45-minute halves.

Abstract

Fatigue monitoring is central in association football due to its links with injury risk and tactical performance. However, objective fatigue-related indicators are commonly derived from subjective self-reported metrics, biomarkers derived from laboratory tests, or, more recently, intrusive sensors such as heart monitors or GPS tracking data. This paper studies whether monocular broadcast videos can provide spatio-temporal signals of sufficient quality to support fatigue-oriented analysis. Building on state-of-the-art Game State Reconstruction methods, we extract player trajectories in pitch coordinates and propose a novel kinematics processing algorithm to obtain temporally consistent speed and acceleration estimates from reconstructed tracks. We then construct acceleration--speed (A-S) profiles from these signals and analyze their behavior as fatigue-related performance indicators. We evaluate the full pipeline on the public SoccerNet-GSR benchmark, considering both 30-second clips and a complete 45-minute half to examine short-term reliability and longer-term temporal consistency. Our results indicate that monocular GSR can recover kinematic patterns that are compatible with A-S profiling while also revealing sensitivity to trajectory noise, calibration errors, and temporal discontinuities inherent to broadcast footage. These findings support monocular broadcast video as a low-cost basis for fatigue analysis and delineate the methodological challenges for future research.

Problem

• athlete fatigue
• monocular video
• association football
• kinematic analysis
• performance monitoring

Innovation

• monocular video analysis
• Game State Reconstruction
• kinematic profiling
• acceleration-speed profile
• athlete fatigue assessment