Abstract
Driver fatigue is the major cause of traffic crashes and financial losses. This paper presents an advanced computer vision and mobile technology using smartphones to monitor visual indicators of driver fatigue, allowing the possibility of making fatigue detection systems more affordable and portable. This technology uses the front camera of a smartphone to capture images of drivers, and then uses advanced computer vision algorithms to detect and track the face and eye of the drivers. Head nod, head rotation and eye blinks are then detected as indicators of driver fatigue. A simulated driving study showed that drowsy drivers differed significantly in the frequency of head nod, head rotation and eye blinks, compared to when they were attentive. The smartphone-based fatigue detection technology may have important applications in reducing drowsiness-related traffic accidents and improving driving safety.
Highlights
Driver fatigue is one of the main causes of traffic crashes
100,000 police-reported crashes were directly caused by driver fatigue, which resulted in about 1,550 deaths, 71,000 injuries, and $12.5 billion financial losses, according to estimation of the National Highway Traffic Safety Administration (NHTSA, 2005)
Driver fatigue was involved in 1.2% to 1.6% of all police-reported crashes and 3.2% of the fatal crashes in the United States (Knipling & Wang, 1995; NHTSA, 1996)
Summary
100,000 police-reported crashes were directly caused by driver fatigue, which resulted in about 1,550 deaths, 71,000 injuries, and $12.5 billion financial losses, according to estimation of the National Highway Traffic Safety Administration (NHTSA, 2005). The National Sleep Foundation estimated in 2002 that 51% of adult drivers had driven a vehicle while drowsy and 17% had fallen asleep behind the wheel These traffic-related deaths and financial losses have encouraged the development of technologies to mitigate the risks of driver fatigue. The relative powers of EEG signals, for example, (α+β)/θ, α/ β, (θ+ α)/ (α+β) and θ/ β, are indicative of driver fatigue [4] Both the camera-based and EEG-based solutions require drivers to purchase special equipment, which limits the popularity of these fatigue detection technologies. A smartphone-based fatigue detection technology would be more portable and affordable than many alternative fatigue detection systems, which use devoted invehicle cameras or EEG sensors [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
