English(EN) Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints

AI系统利用面部和身体关键点监控火车司机警觉性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了一个基于视觉的系统，使用单个RGB摄像头和图神经网络来监控火车司机的警觉性。该系统通过分析面部和上身关键点，将司机状态分为警觉、不警觉和紧急状态。在光照条件下，结合面部和骨骼特征实现了三分类模型81%的最高准确率，区分警觉和不警觉状态的准确率为99%。该研究还引入了一个用于训练和评估此类系统的新数据集。 AI

影响通过提供一种被动、非接触式的方法来监测司机警觉性，有可能提高铁路安全性。

排序理由详细介绍新AI模型和特定应用数据集的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Olivia Nocentini, Marta Lagomarsino, Gokhan Solak, Younggeol Cho, Qiyi Tong, Sara Zeynalpour, Marta Lorenzini, Alessandro Ledda, Arash Ajoudani · 2026-10-07 04:00

基于关键点识别模拟火车司机面部和上半身状态

arXiv:2610.07083v1 Announce Type: cross 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…

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报道来源 [1]

基于关键点识别模拟火车司机面部和上半身状态

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