Driver’s Fatigue Detection based on Eye Tracking

FALGUNI ROY BSSE 0230

Literature Review

Proposed System

Experimental Result

Literature Review

LITERATURE REVIEW

Dynamic Template Matching

Distance of Eyelid

Analyzing the Eye State

Facial Analysis Techniques

PROPOSED SYSTEM

PROPOSED SYSTEM IN BLOCK DIAGRAM

SYSTEM WORKFLOW

ROI EXTRACTION

Face detection

Face Detection Face Area Cropping

ROI EXTRACTION(CONT’D)

Eye Detection

Eye Detection & Cropping

IMAGE PRE-PROCESSING

Histogram Equalize Median Filter

EDGE DETECTION

EYE STATE ANALYSIS

Measure the distance of eyelid

Match the eye image with predefined template

MEASURE THE DISTANCE OF EYELID

x = (image_width/8) y = (image_height/3) height = (3* image_height)/5 width = (3* image_width)/5

MATCH THE EYE IMAGE WITH PREDEFINED TEMPLATE

Use Euclidian Distance

If m =< threshold then we assume it as a close eye and in fatigue state otherwise discard the image

Here m = matching amount between template and image

Experimental

Result

EXPERIMENTAL SETUP

Programming language: Java, javaCV, OpenCV

EclipseVersion:eclipse-jee-helios-win32

OS: 32 bit

LINEAR REPRESENTATION OF THE SYSTEM WITH OPEN EYE

Eye is open and discards the image and continues with the next image

Image pre-processing & edge detection

Eye state analysis

Not an open eye image so pass it for template matching

Match with template

If the 5consecutive close image then the person is in fatigue

Template matching & decision taking

LINEAR REPRESENTATION OF THE SYSTEM WITH CLOSE EYE

ROI EXTRACTION ONLY EYE DETECTION

Case

No.

Length

of Video

Number

of frame

Number of

face image

ROI Extraction with

eye(Eye Detection)

Accuracy

TP/(total number

face image) %TP FN FP TN

Test 1 120 sec 295 295 188 102 5 0 64%

Test 2 90 sec 220 207 140 64 3 0 67%

Test 3 60 sec 145 145 88 50 7 0 61%

Test 4 30 sec 73 63 37 26 0 0 59%

Test 5 15 sec 36 36 21 13 2 0 58%

ROI EXTRACTION WITH FACE BEFORE EYE DETECTION

Case No. Length

of Video

Number

of frame

Number

of face

image

ROI Extraction (Face & Eye

Detection)

Accuracy

TP/(total

number face

image) %Face

Detection

TP FN FP TN

Test 1 120 sec 295 295 242 227 13 2 0 77%

Test 2 90 sec 220 207 187 153 29 5 0 74%

Test 3 60 sec 145 145 135 110 22 3 0 76%

Test 4 30 sec 73 63 50 41 9 0 0 65%

Test 5 15 sec 36 36 34 25 7 2 0 69%

SYSTEM PERFORMANCE MEASUREMENT

Case

No.

Number

of face

image

Open

eye

Close

eye

Eye Detected

(TP)

Open eye Close eye Fatigue Detection

(%)

Open

eye

Close

eye

TP FN TP FN

Test 1 295 165 130 145 82 122 23 69 13 84%

Test 2 207 109 98 100 53 87 13 41 12 77%

Test 3 145 60 85 43 67 38 5 60 7 89%

Test 4 63 45 18 33 12 27 6 9 3 75%

Test 5 36 14 22 10 15 6 4 12 3 80%

Average 81%Fatigue detection = * 100%

FUTURE WORKS

Upgrade the system with eyeglass & both eyes

Upgrade the system with a self light source

Tracking eyes with the head rotation

Update template with driver’s eyes dynamically

ANY QUESTION ??????