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Post on 02-Dec-2014
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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 ??????
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