Robotics 2 AdaBoost for People and Place Detection

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Motivation: People Detection Where are the people?
Motivation: People Detection Where are the people? Why is it hard?Range data contain littleinformation on peopleHard in clutteredenvironments
Page 1 and 2: Robotics 2AdaBoost for Peopleand Pl
Page 3: Motivation: People DetectionPeople
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 and 32: AdaBoost: Step-By-Step Iteration 1,
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
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FeaturesSegment10. Boundary Length1
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Training: Data Labeling Mark segmen
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Training Resulting Training Set+1 -
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AdaBoost: Final Strong ClassifierSt
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ExperimentsEnv. 1 & 2: Corridor and
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Experiments Comparison with hand-tu
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Result: ClassificationTFT F
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Contents Motivation AdaBoost Boosti
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Place Labeling: Motivation Wanted:
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Scenario Example 2 Semantic mapping
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Observations
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ObservationsRoom
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ObservationsRoomDoorway
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Similar Observations
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Classification Problem
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Classification Problem?CorridorRoom
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Representing the Observations A lis
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Simple Features Features of the raw
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Multiple ClassesCorridorRoomDoorway
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Multiple Classes Sequence of binary
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ExperimentsTraining (left)# example
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Application to New EnvironmentTrain
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Robotics 2 AdaBoost for People and Place Detection

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