presented by:
R.RAMKUMAR,
M.SHAKTHIVEL

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DETECTION OF DRIVER FATIGUE USING MECHATRONICS
ABSTRACT
Driver fatigue resulting from sleep deprivation or disorders is an important factor for the increasing number of accidents on today’s road. This paper aims at advancing a system, to detect fatigue symptoms in drivers and produce timely warnings that could prevent accidents. This is an approach for real-time detection of driver’s fatigue. The input to the system is a continuous stream of images from a video camera. The system monitors the driver’s eyes to detect micro-sleeps (lasting for 3 to 4 seconds). The three phases involved in achieving this are localization of the face, tracking of eyes in each frame, detection of failure of tracking. If the eyes remain closed for a period of 3 to 4 seconds, the system determines that the person has fatigue and gives a warning signal.
As the system fails in low lighting conditions, we go for advanced detection system which simultaneously monitors several visual behaviors such as eyelid-movement, pupil movement, and face orientation. These fatigue parameters are combined to form a composite fatigue index that can accurately characterize one’s vigilance level. The eyelid parameters are calculated based on PERCLOS (percentage of eye closure over time) and average eye closure speed.
INTRODUCTION
Driver fatigue resulting from sleep deprivation or sleep disorders is an important factor in the increasing number of accidents on today’s roads. The main purpose was to advance a system to detect fatigue symptoms in drivers and produce timely warnings that could prevent accidents. In the trucking industry, 57% fatal truck accidents are due to driver’s fatigue. The main components of the system consists of a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver’s level of vigilance.
FUNCTIONAL DESCRIPTION
The input to the system are images from a video camera mounted in front of the car, which then analyzes each frame to detect the face region. The face is detected by searching for skin color-like pixels in the image. Then a blob separation performed on the grayscale image helps obtain just the face region. In the eye-tracking phase, the face region obtained from the previous stage is searched for localizing the eyes using a pattern-matching method. Templates, obtained by subtracting two frames and performing a blob analysis on the difference grayscale image, are used for localizing the driver’s eyes.
The eyes are then analyzed to detect if they are open or closed. If the eyes remain closed continuously for more than a certain number of frames, the system decides that the eyes are closed and gives a fatigue alert. It also checks continuously for tracking errors. After detecting errors in tracking, the system starts all over again from face detection. The main focus is on the detection of micro-sleep symptoms. This is achieved by monitoring the eyes of the driver throughout the entire video sequence.
The three phases involved in order to achieve this are the following:
(i) Localization of the face,
(ii) Tracking of eyes in each frame, and
(iii) Detection of failure of tracking.
FACE DETECTION
Normalized chromatic color representations are defined as the normalized r- and g components of the RGB color space. This representation removes the brightness information from the RGB signal while preserving its color. Further, the complexity of the RGB color space is simplified by the dimensional reduction to a simple RG color space. Skin color models vary with the skin color of the people, video cameras used and also with the lighting conditions. Using the skin color model the system filter out the incoming video frames to allow only those pixels with high likelihood of being skin pixels. The system uses a threshold to filter out the skin like pixels from the rest of the image. The filtered image is then binarized and blob operation performed to detect the face region from the rest of the image space. In order to reduce the computational cost and speed up the processing, each incoming frame is sub sampled to a 160x120 frame.
TRACKING OF EYES
The reference eye patterns for each user are recovered previously by taking the difference of two images. The eye blink is used to estimate the position of the eye. The eye templates are recovered by taking a difference of the two images and employing blob (area) operations to isolate the eye regions. For the correct detection of the eye templates, it is required that there is no other motion of the face other than the eye blinks. The eye pattern consists of the eyes centered at the center of the iris of the user. The system searches for the open eyes starting from the left eyes first and then looks for the right eyes. If the scores for the open eyes are reasonably higher than the acceptance level and the system decides that the eyes are open, it does not search for the closed eye patterns in the image.
DETECTION OF FAILURE
The threshold scores fixed for the open eyes consist of a minimum above which the system decides that the eyes are probably open. When the scores are above the maximum threshold, the system decides for sure that the eyes are open and does not search for the closed eyes in the image. But if the scores are between the minimum and the maximum limits, then the system searches the image for closed eyes too in order to remove any mismatches.
In case the eyes of the subject remain closed for unusually long periods of time, the system gives a fatigue alert. The fatigue alert persists as long as the person does not open his eyes. In case all the matches fail, the system decides that there is a tracking failure and switches back to the face localization stage. As the face of the driver does not move a lot between frames, we can use the same region for searching the eyes in the next frame.
DISADVANTAGES
(i) Too small an eye template consisting of just the iris and the sclera for the open eyes and the same size of the template of the closed eyes did not produce very good matches.
(ii) There were mismatches especially in the case of closed eyes as the system finds any part of the skin region as the eye. Thus, there were misses due to incorrect matching with the facial hair for open eyes and other parts of the face for the closed eyes.
(iii)The system could not track the eyes when the subject wears glasses while driving.
(iv) Also the system could not track the eyes, when the subject’s head rotation is above 45 degrees and head tilt up is above
ADVANCED FATIGUE DETECTION SYSTEM
People in fatigue exhibit certain visual behaviors easily observable from changes in their facial features like eyes, head and face. This system can simultaneously and in real time monitor several visual behaviors that typically characterize a person’s level of alertness while driving. These visual cues include eyelid movement, pupil movement, and face orientation. The fatigue parameters computed from these visual cues are subsequently combined to form a composite fatigue index that can robustly, accurately characterize one’s vigilance level.