arXiv：cs.AI· Olivia Nocentini, Marta Lagomarsino, Gokhan Solak, Younggeol Cho, Qiyi Tong, Sara Zeynalpour, Marta Lorenzini, Alessandro Ledda, Arash Ajoudani·· 6 小时前AI 评分29
基于图神经网络的面部与上半身关键点火车司机状态识别
Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints
AI 导读
一项研究提出仅用单个正面 RGB 摄像头和图神经网络识别模拟火车司机状态，将其分类为警觉、非警觉和紧急三类。消融实验显示，在光照条件下融合面部与骨骼特征的三分类模型准确率达 81%，预警/非预警二分类准确率达 99%，优于仅用面部或骨骼特征的模型。研究还发布了一个涵盖三种光照条件的受控 RGB 视频数据集。
正文
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 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.
| Comments: | 11 pages,5 figurees |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07083 [cs.CV] |
| (or arXiv:2610.07083v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07083 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Olivia Nocentini [view email]
[v1]
Mon, 5 Oct 2026 11:25:16 UTC (8,550 KB)
来源：arXiv：cs.AI · arxiv.org