19-07-2012, 03:38 PM
Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue
[attachment=28095]
INTRODUCTION
THE EVER-INCREASING number of traffic accidents in
the United States that are due to a diminished driver’s
vigilance level has become a problem of serious concern
to society. Drivers with a diminished vigilance level suffer
from a marked decline in their perception, recognition, and
vehicle-control abilities and, therefore, pose a serious danger
to their own life and the lives of other people. Statistics show
that a leading cause of fatal or injury-causing traffic accidents
is due to drivers with a diminished vigilance level. In the
trucking industry, 57% of fatal truck accidents are due to driver
fatigue. It is the number one cause of heavy truck crashes.
EYE DETECTION AND TRACKING
Fatigue monitoring starts with extracting visual parameters
that typically characterize a person’s level of vigilance. This is
accomplished via a computer vision system. In this section, we
discuss the computer vision system we developed to achieve
this goal. Fig. 2 provides an overview of our visual-cues extraction
system for driver-fatigue monitoring. The system consists
of two cameras: one wide-angle camera focusing on the
face and another narrow-angle camera focusing on the eyes. The
wide-angle camera monitors head movement and facial expression
while the narrow-angle camera monitors eyelid and gaze
movements. The system starts with eye detection and tracking.
The goal of eye detection and tracking is for subsequent
eyelid-movement monitoring, gaze determination, facial-orientation
estimation, and facial-expression analysis.
EYELID-MOVEMENT PARAMETERS
Eyelid movement is one of the visual behaviors that reflect a
person’s level of fatigue. The primary purpose of eye tracking
is to monitor eyelid movements and to compute the relevant
eyelid-movement parameters. Here, we focus on two ocular
measures to characterize the eyelid movement. The first is
Percentage of eye closure over time (PERCLOS) and the
second is average eye-closure speed (AECS). PERCLOS has
been validated and found to be the most valid ocular parameter
for monitoring fatigue [25].
The eye-closure/opening speed is a good indicator of fatigue.
It is defined as the amount of time needed to fully close or open
the eyes. Our previous study indicates that the eye-closure speed
of a drowsy person is distinctively different from that of an alert
person [37].
FACE (HEAD) ORIENTATION ESTIMATION
The facial (head) pose contains information about one’s attention,
gaze, and level of fatigue. Facial-pose determination is
concerned with computation of the three-dimensional (3-D) facial
orientation and position to detect head movements such as
head tilts. Frequent head tilts indicate the onset of fatigue. Furthermore,
the nominal face orientation while driving is frontal.
If the driver faces in another directions (e.g., down or sideway)
for an extended period of time, this is due to either fatigue or
inattention. Facial-pose estimation, therefore, can indicate both
fatigued and inattentive drivers. For this study, we focus on the
former, i.e., detection of frequent head tilts.
EYE-GAZE DETERMINATION AND TRACKING
Gaze has the potential to indicate a person’s level of vigilance;
a fatigued individual tends to have a narrow gaze. Gaze
may also reveal one’s needs and attention. The direction of a
person’s gaze is determined by two factors: the orientation of
the face (facial pose) and the orientation of eye (eye gaze). Facial
pose determines the global direction of the gaze, while eye
gaze determines the local direction of the gaze. Global and local
gazes together determine the final gaze of the person.
[attachment=28095]
INTRODUCTION
THE EVER-INCREASING number of traffic accidents in
the United States that are due to a diminished driver’s
vigilance level has become a problem of serious concern
to society. Drivers with a diminished vigilance level suffer
from a marked decline in their perception, recognition, and
vehicle-control abilities and, therefore, pose a serious danger
to their own life and the lives of other people. Statistics show
that a leading cause of fatal or injury-causing traffic accidents
is due to drivers with a diminished vigilance level. In the
trucking industry, 57% of fatal truck accidents are due to driver
fatigue. It is the number one cause of heavy truck crashes.
EYE DETECTION AND TRACKING
Fatigue monitoring starts with extracting visual parameters
that typically characterize a person’s level of vigilance. This is
accomplished via a computer vision system. In this section, we
discuss the computer vision system we developed to achieve
this goal. Fig. 2 provides an overview of our visual-cues extraction
system for driver-fatigue monitoring. The system consists
of two cameras: one wide-angle camera focusing on the
face and another narrow-angle camera focusing on the eyes. The
wide-angle camera monitors head movement and facial expression
while the narrow-angle camera monitors eyelid and gaze
movements. The system starts with eye detection and tracking.
The goal of eye detection and tracking is for subsequent
eyelid-movement monitoring, gaze determination, facial-orientation
estimation, and facial-expression analysis.
EYELID-MOVEMENT PARAMETERS
Eyelid movement is one of the visual behaviors that reflect a
person’s level of fatigue. The primary purpose of eye tracking
is to monitor eyelid movements and to compute the relevant
eyelid-movement parameters. Here, we focus on two ocular
measures to characterize the eyelid movement. The first is
Percentage of eye closure over time (PERCLOS) and the
second is average eye-closure speed (AECS). PERCLOS has
been validated and found to be the most valid ocular parameter
for monitoring fatigue [25].
The eye-closure/opening speed is a good indicator of fatigue.
It is defined as the amount of time needed to fully close or open
the eyes. Our previous study indicates that the eye-closure speed
of a drowsy person is distinctively different from that of an alert
person [37].
FACE (HEAD) ORIENTATION ESTIMATION
The facial (head) pose contains information about one’s attention,
gaze, and level of fatigue. Facial-pose determination is
concerned with computation of the three-dimensional (3-D) facial
orientation and position to detect head movements such as
head tilts. Frequent head tilts indicate the onset of fatigue. Furthermore,
the nominal face orientation while driving is frontal.
If the driver faces in another directions (e.g., down or sideway)
for an extended period of time, this is due to either fatigue or
inattention. Facial-pose estimation, therefore, can indicate both
fatigued and inattentive drivers. For this study, we focus on the
former, i.e., detection of frequent head tilts.
EYE-GAZE DETERMINATION AND TRACKING
Gaze has the potential to indicate a person’s level of vigilance;
a fatigued individual tends to have a narrow gaze. Gaze
may also reveal one’s needs and attention. The direction of a
person’s gaze is determined by two factors: the orientation of
the face (facial pose) and the orientation of eye (eye gaze). Facial
pose determines the global direction of the gaze, while eye
gaze determines the local direction of the gaze. Global and local
gazes together determine the final gaze of the person.