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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.02563 |
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| _version_ | 1866910189147389952 |
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| author | Scribano, Carmelo Cappelletti, Giovanni Giacobazzi, Elia Franchini, Giorgia Burgio, Paolo Bertogna, Marko |
| author_facet | Scribano, Carmelo Cappelletti, Giovanni Giacobazzi, Elia Franchini, Giorgia Burgio, Paolo Bertogna, Marko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02563 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network Scribano, Carmelo Cappelletti, Giovanni Giacobazzi, Elia Franchini, Giorgia Burgio, Paolo Bertogna, Marko Computer Vision and Pattern Recognition 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. |
| title | Low-Latency Embedded Driver Monitoring System with a Multi-Task Neural Network |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.02563 |