Real-Time System for Monitoring Driver Vigilance

TLDR

The study introduces a nonintrusive computer‑vision prototype that monitors driver vigilance in real time. The system acquires driver images with an active IR illuminator, extracts six visual metrics (PERCLOS, eye‑closure duration, blink and nodding frequency, face position, fixed gaze), and fuses them via a fuzzy classifier to infer inattentiveness, validated on day and night motorway recordings with multiple users. Fusion of multiple visual parameters yields a more robust and accurate inattention characterization than single‑parameter methods, as shown by experimental results.

Abstract

This paper presents a nonintrusive prototype computer vision system for monitoring a driver's vigilance in real time. It is based on a hardware system for the real-time acquisition of a driver's images using an active IR illuminator and the software implementation for monitoring some visual behaviors that characterize a driver's level of vigilance. Six parameters are calculated: Percent eye closure (PERCLOS), eye closure duration, blink frequency, nodding frequency, face position, and fixed gaze. These parameters are combined using a fuzzy classifier to infer the level of inattentiveness of the driver. The use of multiple visual parameters and the fusion of these parameters yield a more robust and accurate inattention characterization than by using a single parameter. The system has been tested with different sequences recorded in night and day driving conditions in a motorway and with different users. Some experimental results and conclusions about the performance of the system are presented