Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints
Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents a vision-based monitoring system that relies solely on a single front-facing RGB camera and a graph neural network to classify simulated train-driver states into alert, not-alert, and an emergency class comprising acted emergency-like behaviours. To optimize input representations for the model, an ablation study was performed, comparing three feature configurations: skeletal-only, facial-only, and a combination of both. Experimental results show that combining facial and skeletal features yields the highest accur...
| Authors | Olivia Nocentini, Marta Lagomarsino, Gokhan Solak et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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