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235015, 305450 Artificial Intelligence ปัญญาประดิษฐ์ 3(2-2-5). สัปดาห์ที่ 1 ขั้นตอนวิธีเชิงพันธุกรรม (Genetic Algorithm). Outline. 1. Objectives. 2. What is Genetic Algorithm ?. p. 3. Genetic Algorithm Principle. Genetic Algorithm & Application. 4. Objectives.

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235015 305450 artificial intelligence 3 2 2 5

235015, 305450Artificial Intelligenceปัญญาประดิษฐ์3(2-2-5)

สัปดาห์ที่ 1

  • ขั้นตอนวิธีเชิงพันธุกรรม (Genetic Algorithm)
outline
Outline

1

Objectives

2

What is Genetic Algorithm ?

p

3

Genetic Algorithm Principle

Genetic Algorithm & Application

4

objectives
Objectives
  • เพื่อให้นิสิตรู้และเข้าใจในกระบวนการทางพันธุกรรมศาสตร์
  • เพื่อให้นิสิตเรียนรู้และเข้าใจเกี่ยวความสัมพันธ์ของกระบวนการทางพันธุกรรมศาสตร์กับงานด้านคอมพิวเตอร์
  • เพื่อให้นิสิตสามารถประยุกต์ใช้ของกระบวนการทางพันธุกรรมศาสตร์ เพื่อแก้ปัญหาโจทย์ประยุกต์ด้านคอมพิวเตอร์ได้
outline1
Outline

1

Objectives

2

What is Genetic Algorithm ?

p

3

Genetic Algorithm Principle

Genetic Algorithm & Application

4

what is genetic algorithm
What is Genetic Algorithm ?

ไทย: หลักการและประวัติของปัญญาประดิษฐ์ ปริภูมิสถานะและการค้นหา ขั้นตอนวิธีการค้นหาการแทนความรู้โดยใช้ตรรกะเพรดิเคต วิศวกรรมความรู้ โปรล็อกเบื้องต้น การประมวลผลภาษาธรรมชาติเบื้องต้น การเรียนรู้ของเครื่องจักร โครงข่ายประสาทเทียม ขั้นตอนวิธีเชิงพันธุกรรม หุ่นยนต์

อังกฤษ: -

outline2
Outline

1

Objectives

2

What is Genetic Algorithm ?

p

3

Genetic Algorithm Principle

Genetic Algorithm & Application

4

overview of object tracking system
Overview of object tracking system

Input data

Tracking Method

Output data

Trajectory

Tracking

Algorithm

100 frames

Graph of distance100 frames

3

the trajectory based ball detection and tracking
The trajectory-based ball detection and tracking

Input data

Frames Sequence

Output data

slide11

(0,0)

(X3,Y3,D3)

(X1,Y1,D1)

(X2,Y2,D2)

(X4,Y4,D4)

(X5,Y5,D5)

(X6,Y6,D6)

14

fitness value evaluation
Fitness Value Evaluation
  • Where=Euclidean Distance

= X-Coordinate

= Y-Coordinate

fitness value estimation
Fitness value estimation
  • Where=Fitness value per point or frame

= Distance between frame

= Number of population

= Number of frame

46

select the best population
Select the Best Population

Best Population 8 Chromosome

crossover operator
Crossover operator

5

6

1

1

1

4

7

1

6

Possible cross point

4

1

5

Random 20 Chromosome for

Crossing Over

mutation operator
Mutation operator

Random 8 Mutation Chromosome

random operator
Random operator

4 New Random Chromosome

outline3
Outline

1

Objectives

2

What is Genetic Algorithm ?

p

3

Genetic Algorithm Principle

Genetic Algorithm & Application

4

overview of object tracking system1
Overview of object tracking system

Input data

Tracking Method

Output data

Trajectory

Tracking

Algorithm

100 frames

Graph of distance100 frames

3

filtering process
Filtering process
  • The ball candidate objects can be detected by 4 Boolean Function of sieve processes, there are:
    • Color range filter ->(H, S, V)
    • Line filter
    • Shape filter
    • Size filter

11

what is the candidate objects
What is the candidate objects?
  • Where=Boolean Function of Candidate Objects

= Boolean Function of All Objects in Frame

12

ball candidates representation1
Ball candidates representation
  • Where=Candidate Objects in Frame

= X-Coordinate

= Y-Coordinate

= Distance

13

slide37

(0,0)

(X3,Y3,D3)

(X1,Y1,D1)

(X2,Y2,D2)

(X4,Y4,D4)

(X5,Y5,D5)

(X6,Y6,D6)

14

best ball trajectory verification
Best ball trajectory verification

Distance

7

8

1

3

4

5

6

2

Frame No.

16

euclidean distance tracking
Euclidean distance tracking

dE1

dE2

Shortest = dE2

Distance

dE3

Time

k-1

k

k+1

Past

Current

Next

21

example of skeleton trajectory
Example of skeleton trajectory

Kalman Filter -> Temp position

22

miss frame identification
Miss frame identification

Kalman Filter -> Temp position

24

kalman filter process
Kalman Filter Process

dE1> Thd

Prediction

Correction by ROI

Distance

dE2 > Thd

Time

k-1

k

k+1

Past

Current

Future

26

example disadvantage of kalman filter
Example disadvantage of Kalman Filter

“ROI” CUT FOR FINDING SUITABLE OBJECT

27

roi area specification
ROI area specification

ROI

Temp Position-> Kalman Filter

50 pixel

28

roi segmentation
ROI segmentation
  • The propose of ROI segmentation is finding the candidate ball objects in the interesting area by objective function, that compost of 6 parameters there are:
    • 3 o f color parameters (H, S, V) ->Color improvement
    • Distance parameter -> Distance normalization
    • Shape parameter-> Major and minor axis ratio
    • Area parameter -> Average area of previous ball

29

statistical dissimilarity measurement
Statistical Dissimilarity Measurement
  • Where=Statistic dissimilarity measurement

=Mean of interesting object

= Mean of data set

= Variance of interesting object

= Variance of data set

30

statistical similarity
Statistical Similarity
  • Where= Probabilistic value that transfer from

statistic similarity measurement

= Statistic dissimilarity measurement

31

an objective function
An objective function

w1= weight of distance

w2= weight for Hue

w3 = weight for Saturation

w4 = weight for Intensity

w5 = weight for Shape of the object

w6 = weight for Area of the object

3 objects upon to

probability priority

32

color improvement by region reduction
Color improvement by region reduction

(xb,yb)

(xb,yb)

ROI

 yb

 yb

(xc,yc)

(xc,yc)

 xb

 xb

33

type of an objects in roi
Type of an objects in ROI

Type#0

Type#1

Type#4

Type#2

Type#3

34

no object single object in rois
No object & single object in ROIs
  • No object in ROI segmentation is Type#0
  • Single object in ROI segmentation is Type#1

35

many objects in rois
Many objects in ROIs

Type#3

Type#4

Type#2

36

average types values of objects
Average types values of objects
  • Where=Object type

=Integer number represent type of object

= Average value type of each object

37

weight of roi types
Weight of ROI types

Type#3 =

Type#4 =

ROI type = ?

Type#0 =

38

the specification of roi type
The specification of ROI type
  • Where=Region of interest segmentation type

39

multiple trajectory generation
Multiple trajectory generation

Path 1

Path 2

Path 3

Distance

Time

7

8

1

3

4

5

6

2

41

chromosome representation
Chromosome representation
  • a=The number for specific method

c = Index region of frame

e, f = Population number and frame number

b, d = Not use now

43

fitness value estimation1
Fitness value estimation
  • Where=Fitness value per point or frame

= Speed between frame

= Distance between frame

= Number of population

= Number of frame

46

fitness value weight type
Fitness value & weight type
  • Where=Fitness value per point or frame after weight

= Constant weight value

47

best trajectory verification
Best trajectory verification
  • Where=Fitness value per path or all trajectory path

= Best path or best trajectory path

48

best ball trajectory verification1
Best ball trajectory verification

Path 1, F1 = 120

Path 2, F2 = 55

Path 3, F3 = 75

Distance

Time

7

8

1

3

4

5

6

2

49

kalman filter
Kalman Filter

7 Frame

Linear

Distance

Time

7

8

1

3

4

5

6

2

50

cubic spline interpolation
Cubic spline interpolation

7 Frame

Curve

Distance

Time

7

8

1

3

4

5

6

2

51

case of impulse transience
Case of impulse transience

Single-point Impulse Transience

Multi-point Impulse Transience

54

hierarchy adaptive window size technique
Hierarchy adaptive window size technique
  • Where=Threshold = 7.10205255

= Speed between contiguous frame

= Window size

55