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

TL;DR

This paper introduces a lightweight multi-task neural network for real-time driver monitoring, assessing attentiveness and fatigue using a camera-based system suitable for embedded deployment.

Contribution

It presents a novel multi-task neural network that efficiently predicts multiple driver state indicators in a single pass, optimized for low-latency embedded systems.

Findings

Achieves real-time performance on resource-constrained hardware

Accurately predicts driver attentiveness, fatigue, and engagement

Reduces computational load with a multi-task approach

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.