235015, Artificial Intelligence ปัญญาประดิษฐ์ 3(2-2-5) สัปดาห์ที่ 1 ขั้นตอนวิธีเชิงพันธุกรรม (Genetic Algorithm)

Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?

Objectives เพื่อให้นิสิตรู้และเข้าใจในกระบวนการทาง พันธุกรรมศาสตร์ เพื่อให้นิสิตเรียนรู้และเข้าใจเกี่ยวความสัมพันธ์ของ กระบวนการทางพันธุกรรมศาสตร์กับงานด้าน คอมพิวเตอร์ เพื่อให้นิสิตสามารถประยุกต์ใช้ของกระบวนการ ทางพันธุกรรมศาสตร์ เพื่อแก้ปัญหาโจทย์ประยุกต์ ด้านคอมพิวเตอร์ได้

Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?

ไทย : หลักการและประวัติของปัญญาประดิษฐ์ ปริภูมิสถานะและการค้นหา ขั้นตอนวิธีการ ค้นหาการแทนความรู้โดยใช้ตรรกะเพรดิเคต วิศวกรรมความรู้ โปรล็อกเบื้องต้น การ ประมวลผลภาษาธรรมชาติเบื้องต้น การ เรียนรู้ของเครื่องจักร โครงข่ายประสาท เทียม ขั้นตอนวิธีเชิงพันธุกรรม หุ่นยนต์ อังกฤษ : -

Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?

Genetic Algorithm Process

Overview of object tracking system Trajectory Tracking Algorithm 100 frames Graph of distance 100 frames Input dataTracking MethodOutput data 3

The trajectory-based ball detection and tracking Frames Sequence Input data Output data

How to separate the ball ?

(X 1,Y 1,D 1 ) (0,0) (X 2,Y 2,D 2 ) (X 3,Y 3,D 3 ) (X 4,Y 4,D 4 ) (X 5,Y 5,D 5 ) (X 6,Y 6,D 6 ) 14

Ball Candidates Representation 15

Initial Population

Reference Frame Data

Fitness Value Evaluation Where = Euclidean Distance = X-Coordinate = Y-Coordinate

Fitness value estimation Where = Fitness value per point or frame = Distance between frame = Number of population = Number of frame 46

Select the Best Population Best Population 8 Chromosome

Crossover operator Possible cross point Random 20 Chromosome for Crossing Over

Mutation operator Random 8 Mutation Chromosome

Random operator 4 New Random Chromosome

Replace all Offspring in New Generation = 40 ?

Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?

Overview of object tracking system Trajectory Tracking Algorithm 100 frames Graph of distance 100 frames Input dataTracking MethodOutput data 3

How to classify ball from the other objects? 10

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? Where = Boolean Function of Candidate Objects = Boolean Function of All Objects in Frame 12

Ball candidates representation Where = Candidate Objects in Frame = X-Coordinate = Y-Coordinate = Distance 13

(X 1,Y 1,D 1 ) (0,0) (X 2,Y 2,D 2 ) (X 3,Y 3,D 3 ) (X 4,Y 4,D 4 ) (X 5,Y 5,D 5 ) (X 6,Y 6,D 6 ) 14

Input candidates before plot graph 15

Best ball trajectory verification Distance Frame No

Results of segmentation & filtering 17

Position of strength line in frame IndexX- position Y- position DistanceArea

After Background Subtraction 19

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Euclidean distance tracking Distance Time k-1kk+1 d E1 Shortest = d E2 CurrentNext Past d E2 d E3 21

Example of skeleton trajectory 22 Kalman Filter -> Temp position

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Miss frame identification Kalman Filter -> Temp position 24

Kalman filter system 25

Kalman Filter Process Distance Time k-1kk+1 Prediction Correction by ROI CurrentFuture Past d E1 > Th d d E2 > Th d 26

Example disadvantage of Kalman Filter “ROI” CUT FOR FINDING SUITABLE OBJECT 27

ROI area specification 50 pixel ROI 28 Temp Position-> Kalman Filter

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 Where = Statistic dissimilarity measurement = Mean of interesting object = Mean of data set = Variance of interesting object = Variance of data set 30

Statistical Similarity Where = Probabilistic value that transfer from statistic similarity measurement = Statistic dissimilarity measurement 31

An objective function w 1 = weight of distance w 2 = weight for Hue w 3 = weight for Saturation w 4 = weight for Intensity w 5 = weight for Shape of the object w 6 = weight for Area of the object 32 3 objects upon to probability priority

Color improvement by region reduction (x b,y b )  x b  y b (x c,y c ) (x b,y b )  x b  y b (x c,y c ) 33 ROI

Type of an objects in ROI Type#1 Type#2 Type#3 Type#0 Type#4 34

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 Type#2 Type#3Type#4 36

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 ROI type = ? Type#3 = Type#4 = Type#0 = 38

The specification of ROI type Where = Region of interest segmentation type 39

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Multiple trajectory generation Distance Time Path 1 Path 2 Path 3 41

Genetic Algorithm Process 42

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

Initial chromosome or population 44

Reference frame data index region 45

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 Where = Fitness value per point or frame after weight = Constant weight value 47

Best trajectory verification Where = Fitness value per path or all trajectory path = Best path or best trajectory path 48

Best ball trajectory verification Distance Time Path 1, F 1 = 120 Path 2, F 2 = 55 Path 3, F 3 = 75 49

Kalman Filter Distance Time Frame Linear 50

Cubic spline interpolation Distance Time Frame Curve 51

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Example result after previous process 53

Case of impulse transience Single-point Impulse Transience Multi-point Impulse Transience 54

Hierarchy adaptive window size technique Where = Threshold = = Speed between contiguous frame = Window size 55

Example of error before using HAWz 56

Example of refinement result 57

The End 73