1. Eye Tracking for
Reading Detection Algorithm
‫חברת‬ ‫עם‬ ‫בשיתוף‬ ‫נעשה‬ ‫הפרויקט‬Intel.

2. Project Targets
Reading Detection
Line Detection
Experimental Data base

3. System Architecture
• Calibration- Running Points
• Experiment- Running App Full
With Texts
• Sampling & Recording
User’s Gaze Points Data
• Mapping Each Point Into 3
Dimensions
• Algo-
Reading
Detection
‫הבאים‬ ‫השלבים‬ ‫מתרחשים‬ ‫האפליקציה‬ ‫לריצת‬ ‫במקביל‬-

4. Challenges & Solutions
 C-App programing at C# for Windows Mobile platform.
 Eye Tracking System.
 Big Data Base.
 Algorithm-
• Developing an idea.
• Determine parameters.
• Bringing idea into real code.

5. Products
 Apps:
 Experimental App- app that contains a variety of texts and
pictures, simulating daily content and use.
 Tracking App- app that record user’s gaze points
by sampling the eye’s sight location in 30 [Hz].

6. Products- Algorithm
 Raw Data
 Step 1 –
Interpolation &
Averaging
2.4
2.45
2.5
2.55
2.6
2.65
2.7
x 10
5
0 200 400 600 800 1000 1200 1400
0
500
1000
time[msec]
X [pixels]
3D gaze points classification
Y[pixels]
2.4
2.45
2.5
2.55
2.6
2.65
2.7
x 10
5
0 200 400 600 800 1000 1200 1400
0
1000
2000
time[msec]
X [pixels]
3D gaze points classification
Y[pixels]
0 200 400 600 800 1000 1200 1400
0
200
400
600
800
1000
1200
X [pixels]
Y[pixels]
2D gaze points classification
0 200 400 600 800 1000 1200 1400
0
200
400
600
800
1000
1200
X [pixels]
Y[pixels]
2D gaze points classification

7. Products- Algorithm
Cases:
1. (dx & dy) < ReadingValue READING MODE
2. (dx > ReadingValue) & (dy < Reading value) LINE BREAK
 GraphicView –
0 200 400 600 800 1000 1200 1400
0
200
400
600
800
1000
1200
X [pixels]
Y[pixels]
2D gaze points classification
non reading points
line break points
reading pattern points
2.4
2.45
2.5
2.55
2.6
2.65
2.7
x 10
5
0 200 400 600 800 1000 1200 1400
0
1000
2000
time[msec]
X [pixels]
3D gaze points classification
Y[pixels]
non reading points
line break points
reading pattern points

8. Results
App’s first page,
containing 8 lines & title
1.2
1.22
1.24
1.26
1.28
1.3
1.32
1.34
1.36
1.38
1.4
x 10
5
0 200 400 600 800 1000 1200 1400
0
500
1000
time[msec]
X [pixels]
3D gaze points classification
Y[pixels]
1.2
1.22
1.24
1.26
1.28
1.3
1.32
1.34
1.36
1.38
1.4
x 10
5
0 200 400 600 800 1000 1200 1400
0
500
1000
time[msec]
X [pixels]
3D gaze points classification
Y[pixels]
non reading points
line break points
reading pattern points
Experiment
to raw data
Processing &
results

9. Results
Dx
Dy
80100120140
2516.5±4.914.25±3.611.2±3.27.9±2.7
3516.8±4.814.6±3.711.5±3.458.3±2.9
4517±514.7±3.811.6±3.78.5±3.1
5517.4±5.115±3.811.8±3.78.6±3.1
Page 13 in
the app,
text in
english.
Contain 13
lines (no
title).

10. Results
Dx
Dy
80100120140
2526.1±6.5
(26.6±9.7)
22±5.5
(22±7.3)
18.2±4.6
(18.2±6.1)
14.9±4.7
(14.8±5.6)
3526.7±7.1
(27.2±10)
22.5±5.6
(22.5±7.5)
19±4.5
(18.9±6)
15.7±4.6
(15.6±5.5)
4527±7
(27.6±10.6)
22.9±5.7
(23±7.9)
19.3±4.4
(19.4±6.3)
16.1±4.5
(16±5.6)
5527.5±6.9
(11.528.3±)
23.3±5.6
(23.5±8.35)
19.8±4.4
(19.9±6.7)
16.4±4.4
(16.4±6.1)
Page 7 in the
app, text in
hebrew.
Contain 19
lines & title

11. Comparison
Figure 1. pooled evidence algorithm by IBM Almaden Rsearch Center,
San Jose, USA.
Figure 2. our algorithm

12. Future Work
 Enhance Accuracy.
 Real-Time.
 Line detection.