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Example-Based Object Detection in Images by Components. Mohan, Papageorgious and Poggio IEEE PAMI 2001 Presented by Jiun-Hung Chen April 11, 2005. Summary. Goal: Detect objects in static images How: Exampled-based person detection framework by components - PowerPoint PPT Presentation
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Example-Based Example-Based Object Detection in Object Detection in
Images by Images by ComponentsComponentsMohan, Papageorgious and PoggioMohan, Papageorgious and Poggio
IEEE PAMI 2001IEEE PAMI 2001Presented by Jiun-Hung ChenPresented by Jiun-Hung Chen
April 11, 2005April 11, 2005
SummarySummary Goal: Detect objects in static imagesGoal: Detect objects in static images How: Exampled-based person detection How: Exampled-based person detection
framework by componentsframework by components Heads, legs, left arms and right arms detectorsHeads, legs, left arms and right arms detectors Components are present in the proper geometric Components are present in the proper geometric
configurationconfiguration Person detector combines the results of the Person detector combines the results of the
component detectors for person detectioncomponent detectors for person detection Adaptive combination of classifiers (ACC)Adaptive combination of classifiers (ACC) Harr wavelet transform + support vector Harr wavelet transform + support vector
machines (SVM)machines (SVM) Significantly better than a similar full-body Significantly better than a similar full-body
person detectorperson detector
Previous WorkPrevious Work Object detectionObject detection
MModel-basedodel-based IImage invariancemage invariance EExample-basedxample-based
Classifier combination algorithmsClassifier combination algorithms Bagging, Boosting, Voting and so onBagging, Boosting, Voting and so on
Challenges in Person Challenges in Person DetectionDetection
NNonrigid objects, colors, garment typesonrigid objects, colors, garment types RRotated in depth, partially occluded or in motionotated in depth, partially occluded or in motion
System DiagramSystem Diagram
Geometric ConstraintsGeometric Constraints
Harr Wavelet TransformHarr Wavelet Transform
From www.matlab.comFrom www.matlab.com
Support Vector Machines Support Vector Machines (SVM)(SVM)
First, project input data First, project input data nonlinearly and implicitly by nonlinearly and implicitly by kernel functions to a feature kernel functions to a feature spacespace Mercer’s kernels (Polynomial Mercer’s kernels (Polynomial
kernels and Gaussian radial kernels and Gaussian radial basis function kernels)basis function kernels)
Second, find optimal Second, find optimal decision hyperplane in the decision hyperplane in the feature space by maximizing feature space by maximizing soft margins and an upper soft margins and an upper bound of training errorsbound of training errors
The raw output of an SVM The raw output of an SVM classifier is the distance of a classifier is the distance of a data point from the decision data point from the decision hyperplanehyperplane
Training ExamplesTraining Examples
Experimental ResultsExperimental Results
Experimental Results Experimental Results (Cont.)(Cont.)
Experimental Results Experimental Results (Cont.)(Cont.)
Experimental Results Experimental Results (Cont.)(Cont.)
Experimental Results Experimental Results (Cont.)(Cont.)
Learned Geometric Learned Geometric ConstraintsConstraints
ConclusionsConclusions CComponentomponent––based person detectionbased person detection
BBetter than full-body person detectoretter than full-body person detector Hierarchical Classifiers or Adaptive Hierarchical Classifiers or Adaptive
Combination of Classifiers (ACC)Combination of Classifiers (ACC)
Future WorkFuture Work FFace detection: Heisele et al. CVPR’01ace detection: Heisele et al. CVPR’01 Face recognition: Heisele et al. Face recognition: Heisele et al.
CVIU’03CVIU’03 Car detection: Bileschi, Leung and Car detection: Bileschi, Leung and
Rifkin ECCV 04 WorkshopRifkin ECCV 04 Workshop Arbitrary viewpoints?Arbitrary viewpoints?
HHow appearance and geometric ow appearance and geometric configuration changeconfiguration change
QuestionsQuestions LightingLighting Videos Videos
Space-time component based detection, Space-time component based detection, recognition and trackingrecognition and tracking
Other applicationsOther applications InsectInsect
What are meaningful components?What are meaningful components? Object detection/recognition/tracking if Object detection/recognition/tracking if
cameras intrinsic and extrinsic parameters cameras intrinsic and extrinsic parameters may change may change
Thank You! Thank You!