Robotics 2 AdaBoost for People and Place Detection

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Contents Motivation AdaBoost Boosting Features for People Detection Boosting Features for Place Labeling
Problem and Approach Can we find robust features for people, legs andgroups of people in 2D range data? What are the best features for people detection? Can we find people that do not move?Approach: Classifying groups of adjacent beams Computing a set of simple (scalar) features onthese groups Boosting the features
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 and 32: AdaBoost: Step-By-Step Iteration 1,
Page 47: AdaBoost: Wrap Up Misclassified sam
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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