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Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints — arXiv:2610.07083

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07083
License (as archived): CC0-1.0
Archived: 2026-10-07T11:20:14.002Z
SHA-256 of the archived text: e8c1bd132760b2033093d9baa687ebd83230d249229460ae050b0ce293435d4c
Stable URL of this page: https://metraipolis.com/documento/dc_32e032186e53

How to cite

Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints — arXiv:2610.07083 — arXiv (en), https://arxiv.org/abs/2610.07083 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_32e032186e53 (Metraipolis archive dc_32e032186e53, sha256 e8c1bd132760b2033093d9baa687ebd83230d249229460ae050b0ce293435d4c)

BibTeX

@misc{dc32e032186e53,
  title = {Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints — arXiv:2610.07083},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07083},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_32e032186e53; sha256 e8c1bd132760b2033093d9baa687ebd83230d249229460ae050b0ce293435d4c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07083. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 accuracy (81%) for the three-class model under the light condition, outperforming models that use only facial or skeletal features. Furthermore, the combination of facial and skeletal features achieves 99% accuracy in the alert/not alert classification in light condition. Additionally, we introduced a controlled RGB video dataset containing alert, not alert, and acted emergency-like behaviours recorded under three illumination conditions. These contributions represent a step toward passive and non-contact train-driver state recognition based on facial and upper-body dynamics.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.07083 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:15:24.668Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).