Graph-based Online Monitoring of Train Driver States via Facial and Skeletal Features
Journal:
arXiv
Published Date:
May 9, 2025
Abstract
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 an online behavior-based monitoring
system utilizing a customised Directed-Graph Neural Network (DGNN) to classify
train driver's states into three categories: alert, not alert, and
pathological. 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 accuracy (80.88%) in
the three-class model, outperforming models using only facial or skeletal
features. Furthermore, this combination achieves over 99% accuracy in the
binary alertness classification. Additionally, we introduced a novel dataset
that, for the first time, incorporates simulated pathological conditions into
train driver monitoring, broadening the scope for assessing risks related to
fatigue and health. This work represents a step forward in enhancing railway
safety through advanced online monitoring using vision-based technologies.
