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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.05636 |
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| _version_ | 1866908942329708544 |
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| author | Bou, Xavier Correger, Nathan Cloots, Alexandre Gavage, Cédric Giancola, Silvio Schwartz, Cédric Delvaux, François Cloots, Rudi Van Droogenbroeck, Marc Cioppa, Anthony |
| author_facet | Bou, Xavier Correger, Nathan Cloots, Alexandre Gavage, Cédric Giancola, Silvio Schwartz, Cédric Delvaux, François Cloots, Rudi Van Droogenbroeck, Marc Cioppa, Anthony |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05636 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Athlete Fatigue Assessment from Association Football Videos Bou, Xavier Correger, Nathan Cloots, Alexandre Gavage, Cédric Giancola, Silvio Schwartz, Cédric Delvaux, François Cloots, Rudi Van Droogenbroeck, Marc Cioppa, Anthony Computer Vision and Pattern Recognition 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. |
| title | Towards Athlete Fatigue Assessment from Association Football Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.05636 |