10-04-2017, 09:38 PM
source code for how to detect eye drowsiness in matlab
With the ongoing reduction in size and cost of computing and optical monitoring equipment, many governmental and commercial groups are attempting to develop "intelligent automobiles." According to a review of the field, most major automakers have been pursuing some sort of drowsy driver detection, using methods ranging from monitoring the weaving of the car using yaw rate sensors to monitoring the driver's eye with an in-car camera.
Particularly notable is the U.S. Department of Transportation's Intelligent Vehicle Initiative . Intelligent cars are cars that can respond to the state of the driver and increase both safety and convenience. As 90% of accidents occur due to driver error, a great savings in human life and financial loss could be realized by devices that help effective accident avoidance.
Driver drowsiness is one specific form of human error that has been well studied. Studies have shown that immediately prior to fatigue-induced accidents, the driver's eye exhibits a change in blinking behavior. Specifically, the frequency of blinking increases and the percentage of the eye covered by the lid increases. We reproduce a graph summarizing eyelid closure percentage over time before accidents (from Eye-Activity Measures of Fatigue and Napping as a Fatigue Countermeasure,
As the eye closure occurrences dramatically increase during the 10-second period preceding an accident, monitoring such closures could allow the car to take some form of automated response to wake up the driver, e.g. a loud noise, a bright light, possibly even the activation of an "autopilot" if that capability is developed. It is also known that the duration of the eye closures one minute before an accident is much higher than at earlier times. Finally, partial eye closures (measured by the ratio between horizontal and vertical portions of the visible pupil) have been shown to be an excellent way to detect drownsiness, as much as 10-12 minutes prior to an accident (IVI Brochure,
Consequently, we attempted to devise a camera + image processor system to take images of a driver's face and then attempted to process those images to determine whether the eyes were open or closed.
After trying to use cameras in the visible range of the spectrum to detect eye closure, and having considerable difficulty due to false recognition of eyes in the background of the image and due to the large change in image from day to night, we decided to implement an infrared imaging system consisting of a camera and an infrared LED as a source of illumination. This allows several benefits. First, the illumination level can be held constant -- an IR LED can illuminate the driver day and night without distracting the driver. This makes the development of image processing algorithims simpler. Second, we can select the intensity and focus of the IR light such that the driver's face is the only object illuminated. By placing appropriate spectral filters in front of the camera aperture, one could restrict the signal to only the IR light scattered from the face.