用面部和骨骼特征实时监测司机状态，提升铁路安全
Graph-based Online Monitoring of Train Driver States via Facial and Skeletal Features
- 构建定向图神经网络，融合面部与骨骼特征进行状态分类
- 双特征组合在三分类中达80.88%准确率，二分类超99%
- 首次引入模拟病理数据集，拓展疲劳与健康风险评估
司机疲劳是铁路安全的重大挑战，传统系统如死人开关仅提供基础警觉性检测。本研究提出一种基于行为的在线监控系统，采用定制化有向图神经网络（DGNN）将司机状态分为三类：警觉、不警觉和病理状态。为优化模型输入，通过消融实验比较了三种特征配置：仅骨骼、仅面部，以及两者结合。实验表明，结合面部与骨骼特征的模型在三分类任务中达到最高准确率80.88%，优于单一特征模型；在二分类警觉性判断中，准确率超过99%。此外，研究首次构建包含模拟病理条件的列车司机监控数据集，拓展了疲劳与健康风险评估的范围。该工作推动了基于视觉技术的铁路安全在线监控发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。