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.