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235015, 305450 Artificial Intelligence ปัญญาประดิษฐ์ 3(2-2-5) สัปดาห์ที่ 1 ขั้นตอนวิธีเชิงพันธุกรรม (Genetic Algorithm)
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Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?
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Objectives เพื่อให้นิสิตรู้และเข้าใจในกระบวนการทาง พันธุกรรมศาสตร์ เพื่อให้นิสิตเรียนรู้และเข้าใจเกี่ยวความสัมพันธ์ของ กระบวนการทางพันธุกรรมศาสตร์กับงานด้าน คอมพิวเตอร์ เพื่อให้นิสิตสามารถประยุกต์ใช้ของกระบวนการ ทางพันธุกรรมศาสตร์ เพื่อแก้ปัญหาโจทย์ประยุกต์ ด้านคอมพิวเตอร์ได้
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Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?
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ไทย : หลักการและประวัติของปัญญาประดิษฐ์ ปริภูมิสถานะและการค้นหา ขั้นตอนวิธีการ ค้นหาการแทนความรู้โดยใช้ตรรกะเพรดิเคต วิศวกรรมความรู้ โปรล็อกเบื้องต้น การ ประมวลผลภาษาธรรมชาติเบื้องต้น การ เรียนรู้ของเครื่องจักร โครงข่ายประสาท เทียม ขั้นตอนวิธีเชิงพันธุกรรม หุ่นยนต์ อังกฤษ : -
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Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?
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Genetic Algorithm Process
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Overview of object tracking system Trajectory Tracking Algorithm 100 frames Graph of distance 100 frames Input dataTracking MethodOutput data 3
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The trajectory-based ball detection and tracking Frames Sequence Input data Output data
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How to separate the ball ?
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(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
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1 2 10 1 2 1010 Ball Candidates Representation 15
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Initial Population
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Reference Frame Data
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Fitness Value Evaluation Where = Euclidean Distance = X-Coordinate = Y-Coordinate
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Fitness value estimation Where = Fitness value per point or frame = Distance between frame = Number of population = Number of frame 46
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Select the Best Population Best Population 8 Chromosome
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Crossover operator Possible cross point 114415 16 7 561 Random 20 Chromosome for Crossing Over
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Mutation operator Random 8 Mutation Chromosome
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Random operator 4 New Random Chromosome
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Replace all Offspring in New Generation 8 + 20 + 8 + 4 = 40 ?
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Outline Objectives 1 p p 2 Genetic Algorithm Principle 3 Genetic Algorithm & Application 4 What is Genetic Algorithm ?
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Overview of object tracking system Trajectory Tracking Algorithm 100 frames Graph of distance 100 frames Input dataTracking MethodOutput data 3
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How to classify ball from the other objects? 10
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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
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What is the candidate objects? Where = Boolean Function of Candidate Objects = Boolean Function of All Objects in Frame 12
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Ball candidates representation Where = Candidate Objects in Frame = X-Coordinate = Y-Coordinate = Distance 13
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(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
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1 2 10 1 2 1010 Input candidates before plot graph 15
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Best ball trajectory verification Distance Frame No. 1 2 3456 7 8 16
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Results of segmentation & filtering 17
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Position of strength line in frame IndexX- position Y- position DistanceArea 1110.777869.44444129.36699 2186.090970.36364197.661211 3225.363672.31818235.425844 4240.2727156.8182285.535911 5436.8276232493.261329 18
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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
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Example of skeleton trajectory 22 Kalman Filter -> Temp position
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Miss frame identification Kalman Filter -> Temp position 24
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Kalman filter system 25
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Kalman Filter Process Distance Time k-1kk+1 Prediction Correction by ROI CurrentFuture Past d E1 > Th d d E2 > Th d 26
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Example disadvantage of Kalman Filter “ROI” CUT FOR FINDING SUITABLE OBJECT 27
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ROI area specification 50 pixel ROI 28 Temp Position-> Kalman Filter
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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
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Statistical Dissimilarity Measurement Where = Statistic dissimilarity measurement = Mean of interesting object = Mean of data set = Variance of interesting object = Variance of data set 30
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Statistical Similarity Where = Probabilistic value that transfer from statistic similarity measurement = Statistic dissimilarity measurement 31
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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
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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
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Type of an objects in ROI Type#1 Type#2 Type#3 Type#0 Type#4 34
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No object & single object in ROIs No object in ROI segmentation is Type#0 Single object in ROI segmentation is Type#1 35
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Many objects in ROIs Type#2 Type#3Type#4 36
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Average types values of objects Where = Object type = Integer number represent type of object = Average value type of each object 37
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Weight of ROI types ROI type = ? Type#3 = Type#4 = Type#0 = 38
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The specification of ROI type Where = Region of interest segmentation type 39
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Multiple trajectory generation Distance Time 1 2 3456 7 8 Path 1 Path 2 Path 3 41
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Genetic Algorithm Process 42
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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
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Initial chromosome or population 44
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Reference frame data index region 45
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Fitness value estimation Where = Fitness value per point or frame = Speed between frame = Distance between frame = Number of population = Number of frame 46
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Fitness value & weight type Where = Fitness value per point or frame after weight = Constant weight value 47
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Best trajectory verification Where = Fitness value per path or all trajectory path = Best path or best trajectory path 48
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Best ball trajectory verification Distance Time 1 2 3456 7 8 Path 1, F 1 = 120 Path 2, F 2 = 55 Path 3, F 3 = 75 49
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Kalman Filter Distance Time 1 2 3456 7 8 7 Frame Linear 50
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Cubic spline interpolation Distance Time 1 2 3456 7 8 7 Frame Curve 51
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Example result after previous process 53
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Case of impulse transience Single-point Impulse Transience Multi-point Impulse Transience 54
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Hierarchy adaptive window size technique Where = Threshold = 7.10205255 = Speed between contiguous frame = Window size 55
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Example of error before using HAWz 56
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Example of refinement result 57
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The End 73
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