Real-Time System for Monitoring Driver Vigilance

Abstract

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.Index Terms-Driver vigilance, eyelid movement, face position, fuzzy classifier, percent eye closure (PERCLOS), visual fatigue behaviors.

Video-based observer rated sleepiness versus self-reported subjective sleepiness in real road driving

“…Much research is currently devoted to developing automatic image processing systems capable of determining the level of sleepiness based on characteristics such as facial tone, slow eyelid closure, rubbing, yawning and nodding [35][36][37][38][39]. According to Vural et al [40], the ten facial actions that are most predictive of sleepiness are increased blink/eye closure, elevated outer brow raise, increased frown, chin raise, more nose wrinkle, less smiling, tightened eye lid, less compressed nostrils, less lowering of eye brows and less jaw drop.…”

Automatic gaze-based user-independent detection of mind wandering during computerized reading

“…It is driven in part by the desire to increase user productivity, safety, and satisfaction by modeling attention. Knowledge of a user's attentional state can be used for a number of purposes, but common applications aim at improving productivity in real world environments such as an office space (Ba and Odobez 2006;Börner et al 2014;Dong et al 2010;Horvitz et al 1999Horvitz et al , 2003Matsumoto et al 2000;Selker 2004;Stiefelhagen et al 2001;Stiefelhagen 2002;Stiefelhagen and Zhu 2002;Vertegaal et al 2006;Voit and Stiefelhagen 2008), monitoring driver inattention to increase safety (Bergasa et al 2006;Dong et al 2011;D'Orazio et al 2007;Fletcher and Zelinsky 2009;Knipling et al 1994;Su et al 2006;Tawari et al 2014;Torkkola et al 2004;Yeo et al 2009), and improving interaction in virtual environments (Barbuceanu et al 2011;Horvitz et al 2003;Muir and Conati 2012;Navalpakkam et al 2012;Roda and Nabeth 2007;Toet 2006;Vertegaal et al 2006;Ugurlu 2014;Yonetani et al 2012).…”

“…As such, there have been a number of studies on driver inattention, specifically distractibility and fatigue (Dong et al 2011). In addition to gaze data (Bergasa et al 2006;Fletcher and Zelinsky 2009;Su et al 2006;Tawari et al 2014), a wide variety of other methods have been used to estimate the attentional state of the driver. Some examples include neurophysiological measures such as EEG (Yeo et al 2009), driving context measures such as seat pressure and steering angle (Furugori et al 2005;Torkkola et al 2004), and analyses of the position and closures of the eyes (D'Orazio et al 2007).…”

Real-Time Driver’s Biological Signal Monitoring System

“…According to the U.S. National Highway Traffic Safety Administration (NHTSA), falling asleep while driving is responsible for at least 100000 automobile crashes annually, resulting in annual averages of roughly 40000 nonfatal injuries and 1550 fatalities. (1) The National Sleep Foundation has also reported that 60% of adult drivers have driven while feeling drowsy, and 37% have even actually fallen asleep at the wheel. (2) For this reason, various methods have been developed for monitoring the state of vigilance of drivers to avoid accidents related to driver drowsiness.…”