Drowsiness Detection System Using Webcam
Prepared by: Badiuzaman Bin Baharu
- 15165 -Supervisor:
Dr. Nasreen Bt. Badruddin
Background Of StudyNumbers of accidents by types of vehicles in Malaysia,
2005 – 2009
Main Causes
Fatigue
Drowsiness
ObjectivesTo investigate the physical changes of drowsiness that can be
captured by webcam and meet the features:
- Detect drowsiness signs
- Fast
- Accurate
The analysis of the physical changes includes:
- Eye blink pattern
- Yawning
Scope Of StudyData collecting
- Video is being recorded to be use as data.
Video analysis
- To detect the drowsiness signs each frame of the video.
Algorithm development
- To develop the specific command algorithm only for the
video.
Problem StatementCurrent method to detect drowsiness- Complex computation. - Complex and expensive equipment.- Not comfortable and suitable to
use in real-time driving .
Electroencephalography (EEG)
Relevancy Of The Project
Can be implement and be patent to be use in Malaysia.
Aiming to reduce the numbers of fatal or non-fatal road
accidents.
To reduce the risk on the roads, so it is safe to be use by
other people.
Literature Review
1. What is fatigue?
2. What is drowsiness?
3. Electroencephalography (EEG) method
4. Eye blink pattern method
5. PERCLOS method
6. Yawning method
What is fatigue?
Tired; mental & physical. [1]
Mental fatigue leads to drowsiness.
Decrease of physiological arousal. (movement)
Sensorimotor functions slower. (alertness)
Driver’s ability to respond to a situation decrease. (reflects)
[1] . G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness," Neuroscience & Biobehavioral Reviews, 2012.
What is drowsiness?
A state of near sleep.
Strong desire to sleep.
Cannot give full attention or focus on something. [1]
Under influence of drowsiness is not in alert state. [1]
[1]. G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness," Neuroscience & Biobehavioral Reviews, 2012.
Electroencephalography (EEG)Measuring the brain electrical activity. [2]
Can measure heartbeat, eye blink, even major physical movement.
Use special hardware on the scalp to sense the electrical brain activity.
The best method to applied in detecting fatigue and drowsiness.
Sensor too sensitive with noise.
[2]. B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, "Using EEG spectral components to assess algorithms for detecting fatigue," Expert Systems with Applications, vol. 36, pp. 2352-2359, 2009.
Eye blink patternLearned the eye blink pattern of the duration of the
eyelid were closed. [3]The longer times it takes, it is possible the person is
asleep.Measures the position of eyelid and iris.Not detect drowsiness, predict drowsiness by using eye
closing time = awake/fall asleep.
[3]. T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, "Drowsy driver detection system using eye blink patterns," in Machine and Web Intelligence (ICMWI), 2010 International Conference on, 2010, pp. 230-233.
Average person eye blink duration is < 400ms.
Average eye blink is 75ms.
The set point of drowsiness time taken as consideration
in this project is Tdrowsy = 400ms. Tsleeping = 800ms.
PERCLOSPERcentage of eye CLOSure. [4]Calculating the percentage of eyelid droops.Drowsy eyelid droops take times.If eyelid is 80% droops, it is consider as drowsy and fall
asleep.Must use special camera to detect iris position.
[4]. D. F. Dinges and R. Grace, "PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance," Federal Highway Administration. Office of motor carriers, Tech. Rep. MCRT-98-006, 1998.
Yawning
Detect the mouth positioning. [5]
Compared with set of images data for mouth and
yawning.
A person will take several times before close their mouth
while yawning.
[5]. M. Saradadevi and P. Bajaj, "Driver fatigue detection using mouth and yawning analysis,"
IJCSNS International Journal of Computer Science and Network Security, vol. 8, pp. 183-188,
2008.
Method/ Advantages &
Disadvantages
EEG Eye blink pattern
PERCLOS Yawning
Complex method Y N N N
Expensive hardware
Y N Y N
Special hardware Y N Y N
Comfortable N Y Y Y
Suitable in real-time driving
N Y Y Y
Therefore, the eye blink pattern and yawning method will be used in this project based by its advantages and disadvantages table.
Methodology
START
ERROR
SUCCESS
NO
HARDWARE SELECTION SOFTWARE SELECTION
DATA COLLECTION
END
CHANGES
EYE BLINK PATTERN YAWNING
DETECT DROWSINESS
SIGNS?
ALGORITHM DEVELOPMENT AND TESTING
YES
ALGORITHM TROUBLESHOOTING AND
IMPROVEMENT
Gantt Chart & Key Milestones
References [1]. G. Borghini, L. Astolfi, G. Vecchiato, D. Mattia, and F. Babiloni, "Measuring
neurophysiological signals in aircraft pilots and car drivers for the assessment of mental
workload, fatigue and drowsiness," Neuroscience & Biobehavioral Reviews, 2012.
[2]. B. T. Jap, S. Lal, P. Fischer, and E. Bekiaris, "Using EEG spectral components to assess
algorithms for detecting fatigue," Expert Systems with Applications, vol. 36, pp. 2352-2359,
2009.
[3]. T. Danisman, I. M. Bilasco, C. Djeraba, and N. Ihaddadene, "Drowsy driver detection
system using eye blink patterns," in Machine and Web Intelligence (ICMWI), 2010
International Conference on, 2010, pp. 230-233.
[4]. D. F. Dinges and R. Grace, "PERCLOS: A valid psychophysiological measure of alertness as
assessed by psychomotor vigilance," Federal Highway Administration. Office of motor
carriers, Tech. Rep. MCRT-98-006, 1998.
[5]. M. Saradadevi and P. Bajaj, "Driver fatigue detection using mouth and yawning analysis,"
IJCSNS International Journal of Computer Science and Network Security, vol. 8, pp. 183-188,
2008.
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