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AI system monitors train driver alertness using facial and body keypoints

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a vision-based system for monitoring train driver alertness using a single RGB camera and a graph neural network. This system classifies driver states into alert, not-alert, and an emergency class by analyzing facial and upper-body keypoints. Combining both facial and skeletal features achieved the highest accuracy of 81% for the three-class model under light conditions, and 99% for distinguishing between alert and not-alert states. The study also introduced a new dataset for training and evaluating such systems. AI

IMPACT Could enhance railway safety by providing a passive, non-contact method for monitoring driver alertness.

RANK_REASON Academic paper detailing a new AI model and dataset for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI system monitors train driver alertness using facial and body keypoints

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18 / 100
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Academic paper detailing a new AI model and dataset for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety, product
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High
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COVERAGE [1]

  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 ·

    Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints

    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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