A REAL-TIME DEFORMABLE DETECTOR 謝汝欣

OUTLINE  Introduction  Related Work  Proposed Method  Experiments 2

OUTLINE  Introduction - Object detection - Challenge  Related Work  Proposed Method  Experiments 3

OBJECT DETECTION  Human Detection 4

OBJECT DETECTION  Hand Detection 5

OUTLINE  Introduction - Object detection - Challenge  Related Work  Proposed Method  Experiments 6

CHALLENGE  Changes in appearance - Location - Scale - In-plane rotations - Out-of-plane rotations - Viewpoint changes - Deformations - Variations in illumination 7

OUTLINE  Introduction  Related Work  Proposed Method  Experiments 8

OUTLINE  Introduction  Related Work - A collection of detectors - Pyramid System - Pose-Index feature  Proposed Method  Experiments 9

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. 10

A COLLECTION OF DETECTORS A zero-background classifier A one-background classifier A three-background classifier A five-background classifier Combination Hand 11

OUTLINE  Introduction  Related Work - A collection of detectors - Pyramid System - Pose-Index feature  Proposed Method  Experiments 12

PYRAMID SYSTEM  Pose estimation at first stage.  Pose-dedicated classifier at second stage. Five Classifier Hand Pose estimator One Classifier Hand Estimate 5 Estimate 1 13

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 classifierFive classifier 14

OUTLINE  Introduction  Related Work - A collection of detectors - Pyramid System - Pose-Index feature  Proposed Method  Experiments 15

POSE-INDEX FEATURE  Allowing features to be parameterized with the pose.  Need exhaustive pose exploration in testing. 16

POSE-INDEX FEATURE  Training Labeled Zero Labeled One Labeled ThreeLabeled Five Pose-Index Feature parameterized with the pose. 17

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. 18

PROBLEM  Require the training data to be labeled.  Need exploration of pose parameters in testing. Labeled Zero Labeled One Labeled ThreeLabeled Five Training & Testing Dataset 19

OUTLINE  Introduction  Related Work  Proposed Method  Experiments 20

OUTLINE  Introduction  Related Work  Proposed Method - Main Idea - Framework - Implementation Details  Experiments 21

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. 22

OUTLINE  Introduction  Related Work  Proposed Method - Main Idea - Definition - Framework - Implementation Details  Experiments 23

DEFINITION 24

DEFINITION 25

OUTLINE  Introduction  Related Work  Proposed Method - Main Idea - Framework - Implementation Details  Experiments 26

FRAMEWORK Edge Detector frame Pose-Indexed Feature 0/1 Final Detector Pose Estimator 27

OUTLINE  Introduction  Related Work  Proposed Method - Main Idea - Framework - Implementation Details  Experiments 28

IMPLEMENTATION DETAILS Edge Detector frame Pose-Indexed Feature 0/1 Final Detector Pose Estimator 29

IMPLEMENTATION DETAILS 30

IMPLEMENTATION DETAILS 8 bins Input frame 31

IMPLEMENTATION DETAILS Edge Detector frame Pose-Indexed Feature 0/1 Final Detector Pose Estimator 32

IMPLEMENTATION DETAILS 14 Pose Estimators 33

IMPLEMENTATION DETAILS  Pose Estimators - 1 st Pose Estimator h 1 =0.08h 2 =0.15h 3 =0.12h 4 =0.09 h 5 =0.06h 8 =0.11h 7 =0.18 h 6 = bins Input frame l=(u,v) 34

IMPLEMENTATION DETAILS  Pose Estimators - 2 nd Pose Estimator h 1 =0.05h 2 =0.12h 3 =0.18h 4 =0.02 h 5 =0.05h 8 =0.10 h 6 =0.16 h 7 = bins Input frame l=(u,v) 35

IMPLEMENTATION DETAILS Edge Detector frame Pose-Indexed Feature 0/1 Final Detector Pose Estimator 36

IMPLEMENTATION DETAILS 37

IMPLEMENTATION DETAILS 8 bins Input frame g 1 =0.06g 2 =0.17g 3 =0.18g 4 =0.09 g 5 =0.04g 8 =0.11 g 6 =0.15 g 7 =0.20 l=(u,v) 38

IMPLEMENTATION DETAILS 8 bins Input frame g 1 =0.03g 2 =0.15g 3 =0.16g 4 =0.03 g 5 =0.04g 8 =0.17 g 6 =0.13 g 7 =0.28 l=(u,v) 39

IMPLEMENTATION DETAILS Edge Detector frame Pose-Indexed Feature 0/1 Final Detector Pose Estimator 40

IMPLEMENTATION DETAILS 41

OUTLINE  Introduction  Related Work  Proposed Method  Experiments 42

OUTLINE  Introduction  Related Work  Proposed Method  Experiments - Aerial Images of Cars - Face Images - Hand Video Sequence 43

EXPERIMENTS  Aerial Images of Cars 44

OUTLINE  Introduction  Related Work  Proposed Method  Experiments - Aerial Images of Cars - Face Images - Hand Video Sequence 45

EXPERIMENTS  Face Images 46

OUTLINE  Introduction  Related Work  Proposed Method  Experiments - Aerial Images of Cars - Face Images - Hand Video Sequence 47

EXPERIMENTS  Hand Video Sequence 48

REFERENCE  “A Real-Time Deformable Detector,” Karim Ali, Franc¸ois Fleuret, David Hasler, and Pascal Fua, IEEE Transactions on Pattern Analysis and Machine Intelligence