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arXiv · 2605.02563

Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network

Carmelo Scribano·Giovanni Cappelletti·Elia Giacobazzi·Giorgia Franchini·Paolo Burgio·Marko Bertogna
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Abstract

Road traffic accidents remain a significant global concern, with the majority attributed to human factors such as driver distraction and fatigue. This study proposes a camera-based approach to derive useful indicators to assess driver attentiveness and alertness. The proposed pipeline jointly satisfies the stringent real-time requirements imposed by the critical application and minimizes the computational requirements to allow for deployment on a tight computational budget. To this end, we develop a lightweight multi-task neural network that predicts multiple indicators for the face region in a single forward pass. The developed model is integrated into a complete execution workflow to produce a real-time estimate of attentiveness, fatigue, and engagement in distracting activities.

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Carmelo Scribano, Giovanni Cappelletti, Elia Giacobazzi, Giorgia Franchini, Paolo Burgio, Marko Bertogna. 2026-05-04. Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network. https://arxiv.org/abs/2605.02563

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