A REAL-TIME DEFORMABLE

DETECTOR謝汝欣 20131114

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OUTLINE

Introduction

Related Work

Proposed Method

Experiments

3

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

21

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.

26

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

43

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.