A REAL-TIME DEFORMABLE
DETECTOR謝汝欣 20131114
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments
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OUTLINE
Introduction- Object detection- Challenge
Related Work
Proposed Method
Experiments
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OBJECT DETECTION
Human Detection
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OBJECT DETECTION
Hand Detection
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OUTLINE
Introduction- Object detection- Challenge
Related Work
Proposed Method
Experiments
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CHALLENGE
Changes in appearance- Location- Scale- In-plane rotations- Out-of-plane rotations- Viewpoint changes- Deformations - Variations in illumination
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments
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OUTLINE
Introduction
Related Work- A collection of detectors- Pyramid System- Pose-Index feature
Proposed Method
Experiments
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A COLLECTION OF DETECTORS Combine a collection of classifiers , each dedicated to a single pose.
- A zero-background classifier- A one-background classifier- A three-background classifier- A five-background classifier
A classifier which can detect 0,1,3,5 hand posture.
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A COLLECTION OF DETECTORS
A zero-background classifier
A one-background classifier
A three-background classifier
A five-background classifier
Combination
Hand
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OUTLINE
Introduction
Related Work- A collection of detectors- Pyramid System- Pose-Index feature
Proposed Method
Experiments
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PYRAMID SYSTEM
Pose estimation at first stage.
Pose-dedicated classifier at second stage.
Five Classifier
HandPose
estimatorOne
ClassifierHand
Estimate 5
Estimate 1
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PROBLEM
Training data must be appropriately annotated in order for them to be partitioned into clusters of similar poses.
Partitioning of the available training data reduces the number of samples used to train each pose-dedicated classifier.
Zero classifier One classifier Three classifier Five classifier
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OUTLINE
Introduction
Related Work- A collection of detectors- Pyramid System- Pose-Index feature
Proposed Method
Experiments
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POSE-INDEX FEATURE
Allowing features to be parameterized with the pose.
Need exhaustive pose exploration in testing.
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POSE-INDEX FEATURE
Training
Labeled Zero Labeled One Labeled Three Labeled Five
Pose-Index Feature parameterized with the pose.
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POSE-INDEX FEATURE
Testing
Pose-index feature
Hand
Feature parameterized by zero hand posture.
Feature parameterized by one hand posture.
Feature parameterized by three hand posture.
Feature parameterized by five hand posture.
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PROBLEM
Require the training data to be labeled.
Need exploration of pose parameters in testing.
Labeled Zero Labeled One Labeled Three Labeled Five
Training & Testing Dataset
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments
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OUTLINE
Introduction
Related Work
Proposed Method- Main Idea- Framework- Implementation Details
Experiments
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MAIN IDEA
Use the pose-indexed features- Training proceeds on the unpartitioned dataset.
Pose-estimator learning and feature learning
occur jointly.- No need to label for training data.- No need to exploration of these pose parameters in testing.
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OUTLINE
Introduction
Related Work
Proposed Method- Main Idea- Framework- Implementation Details
Experiments
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FRAMEWORK
Edge Detector
frame
Pose-IndexedFeature
0/1Final Detector
Pose Estimator
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OUTLINE
Introduction
Related Work
Proposed Method- Main Idea- Framework- Implementation Details
Experiments
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IMPLEMENTATION DETAILS
Edge Detector
frame
Pose-IndexedFeature
0/1Final Detector
Pose Estimator
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IMPLEMENTATION DETAILS Edge Detector
- : Possible Orientations of a quantized edge.
- : The presence of an edge with quantized orientation e at pixel l in image x.
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IMPLEMENTATION DETAILS Edge Detector
-
8 bins
Input frame
𝜉1=0 𝜉2=1 𝜉3=0 𝜉4=0
𝜉5=0 1 𝜉7=1 𝜉8=0
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IMPLEMENTATION DETAILS
Edge Detector
frame
Pose-IndexedFeature
0/1Final Detector
Pose Estimator
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IMPLEMENTATION DETAILS Pose Estimators
- : Computes the dominate edge orientation in the window translated according to (u,v).
-
14 Pose Estimators
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IMPLEMENTATION DETAILS Pose Estimators - 1st Pose Estimator
𝜃2=5𝜋4
h1=0.08 h2=0.15 h3=0.12 h4=0.09
h5=0.06 h8=0.11h7=0.18h6=0.21
8 bins Input frame
l=(u,v)
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IMPLEMENTATION DETAILS Pose Estimators - 2nd Pose Estimator
𝜃2=7𝜋4
h1=0.05 h2=0.12 h3=0.18 h4=0.02
h5=0.05 h8=0.10h6=0.16 h7=0.32
8 bins Input frame
l=(u,v)
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IMPLEMENTATION DETAILS
Edge Detector
frame
Pose-IndexedFeature
0/1Final Detector
Pose Estimator
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IMPLEMENTATION DETAILS Pose-Indexed Feature
- : A rectangular window in the image plane obtained by applying a rotation of angle and a translation ( u , v )
- The proportion of edges with a rotated edge orientation in the translated and the rotated rectangular window.
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IMPLEMENTATION DETAILS Pose-Indexed Feature
- For 1st pose estimator ,
8 bins Input frame
g1=0.06 g2=0.17 g3=0.18 g4=0.09
g5=0.04 g8=0.11g6=0.15 g7=0.20
l=(u,v)
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IMPLEMENTATION DETAILS Pose-Indexed Feature
- For 2nd pose estimator ,
8 bins Input frame
g1=0.03 g2=0.15 g3=0.16 g4=0.03
g5=0.04 g8=0.17g6=0.13 g7=0.28
l=(u,v)
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IMPLEMENTATION DETAILS
Edge Detector
frame
Pose-IndexedFeature
0/1Final Detector
Pose Estimator
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IMPLEMENTATION DETAILS Final detector
- Ex : AdaBoost Classifier
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments - Aerial Images of Cars- Face Images- Hand Video Sequence
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EXPERIMENTS
Aerial Images of Cars
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments - Aerial Images of Cars- Face Images- Hand Video Sequence
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EXPERIMENTS
Face Images
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OUTLINE
Introduction
Related Work
Proposed Method
Experiments - Aerial Images of Cars- Face Images- Hand Video Sequence
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EXPERIMENTS
Hand Video Sequence
https://www.youtube.com/watch?v=NbeHYxRNtAw
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REFERENCE
“A Real-Time Deformable Detector,” Karim Ali, Franc¸ois Fleuret, David Hasler, and Pascal Fua, IEEE Transactions on Pattern Analysis and Machine Intelligence 2012.