thesis
DESCRIPTION
TRANSCRIPT
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Driver’s Fatigue Detection based on Eye Tracking
FALGUNI ROY BSSE 0230
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Literature Review
Proposed System
Experimental Result
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Literature Review
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LITERATURE REVIEW
Dynamic Template Matching
Distance of Eyelid
Analyzing the Eye State
Facial Analysis Techniques
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PROPOSED SYSTEM
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PROPOSED SYSTEM IN BLOCK DIAGRAM
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SYSTEM WORKFLOW
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ROI EXTRACTION
Face detection
Face Detection Face Area Cropping
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ROI EXTRACTION(CONT’D)
Eye Detection
Eye Detection & Cropping
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IMAGE PRE-PROCESSING
Histogram Equalize Median Filter
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EDGE DETECTION
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EYE STATE ANALYSIS
Measure the distance of eyelid
Match the eye image with predefined template
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MEASURE THE DISTANCE OF EYELID
x = (image_width/8) y = (image_height/3) height = (3* image_height)/5 width = (3* image_width)/5
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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
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Experimental
Result
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EXPERIMENTAL SETUP
Programming language: Java, javaCV, OpenCV
EclipseVersion:eclipse-jee-helios-win32
OS: 32 bit
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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
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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
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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%
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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%
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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%
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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
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ANY QUESTION ??????
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