Graph-based Online Monitoring of Train Driver States via Facial and Skeletal Features
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908362415800320 |
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| author | Nocentini, Olivia Lagomarsino, Marta Solak, Gokhan Cho, Younggeol Tong, Qiyi Lorenzini, Marta Ajoudani, Arash |
| author_facet | Nocentini, Olivia Lagomarsino, Marta Solak, Gokhan Cho, Younggeol Tong, Qiyi Lorenzini, Marta Ajoudani, Arash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08800 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-based Online Monitoring of Train Driver States via Facial and Skeletal Features Nocentini, Olivia Lagomarsino, Marta Solak, Gokhan Cho, Younggeol Tong, Qiyi Lorenzini, Marta Ajoudani, Arash Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Graph-based Online Monitoring of Train Driver States via Facial and Skeletal Features |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.08800 |