Robotics 2 AdaBoost for People and Place Detection

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<strong>AdaBoost</strong>: Step-By-Step Iteration 2, train weak classifier 2Thresholdθ* = 0.47Dimensionj* = 2Weighted errore m = 0.16Voting weightα m = 1.69Total error = 5
<strong>AdaBoost</strong>: Step-By-Step Iteration 2, recompute weightsThresholdθ* = 0.47Dimensionj* = 2Weighted errore m = 0.16Voting weightα m = 1.69Total error = 5
Page 1 and 2: Robotics 2AdaBoost for Peopleand Pl
Page 3 and 4: Motivation: People DetectionPeople
Page 5 and 6: Motivation: People Detection Where
Page 7 and 8: Motivation: People Detection Appear
Page 9 and 10: Motivation: People DetectionFreibur
Page 11 and 12: Motivation: People Detection Freibu
Page 13 and 14: Contents Motivation AdaBoost Boosti
Page 15 and 16: AdaBoost What AdaBoost can do for y
Page 17 and 18: ClassificationLinear vs non-linear
Page 19 and 20: ClassificationError typesTrue value
Page 21 and 22: AdaBoost: Weak ClassifierDecision s
Page 23 and 24: AdaBoost: Strong Classifier Computi
Page 25 and 26: AdaBoost: Weak Classifier Train a d
Page 27 and 28: AdaBoost: Weak ClassifierTraining a
Page 29 and 30: AdaBoost: Step-By-Step Training dat
Page 31: AdaBoost: Step-By-Step Iteration 1,
Page 35 and 36: AdaBoost: Step-By-Step Iteration 3,
Page 47 and 48: AdaBoost: Wrap Up Misclassified sam
Page 49 and 50: Problem and Approach Can we find ro
Page 51 and 52: Segmentation Divide the scan into s
Page 53 and 54: SegmentationRaw scanSegmented scan
Page 55 and 56: FeaturesSegment1. Number of points2
Page 57 and 58: FeaturesSegment10. Boundary Length1
Page 59 and 60: Training: Data Labeling Mark segmen
Page 61 and 62: Training Resulting Training Set+1 -
Page 63 and 64: AdaBoost: Final Strong ClassifierSt
Page 65 and 66: ExperimentsEnv. 1 & 2: Corridor and
Page 67 and 68: Experiments Comparison with hand-tu
Page 69 and 70: Result: ClassificationTFT F
Page 71 and 72: Contents Motivation AdaBoost Boosti
Page 73 and 74: Place Labeling: Motivation Wanted:
Page 75 and 76: Scenario Example 2 Semantic mapping
Page 77 and 78: Observations
Page 79 and 80: ObservationsRoom
Page 81 and 82: ObservationsRoomDoorway
Page 83 and 84:
Similar Observations
Page 85 and 86:
Classification Problem
Page 87 and 88:
Classification Problem?CorridorRoom
Page 89 and 90:
Representing the Observations A lis
Page 91 and 92:
Simple Features Features of the raw
Page 93 and 94:
Multiple ClassesCorridorRoomDoorway
Page 95 and 96:
Multiple Classes Sequence of binary
Page 97 and 98:
ExperimentsTraining (left)# example
Page 99 and 100:
Application to New EnvironmentTrain
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Robotics 2 AdaBoost for People and Place Detection

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