A Real-Time Deformable Detector

1. A Real-Time Deformable Detector 謝汝欣 20131114

2. Outline • Introduction • Related Work • Proposed Method • Experiments

3. Outline • Introduction • Object detection • Challenge • Related Work • Proposed Method • Experiments

4. Object Detection • Human Detection

5. Object Detection • Hand Detection

6. Outline • Introduction • Object detection • Challenge • Related Work • Proposed Method • Experiments

7. Challenge • Changes in appearance • Location • Scale • In-plane rotations • Out-of-plane rotations • Viewpoint changes • Deformations • Variations in illumination

8. Outline • Introduction • Related Work • Proposed Method • Experiments

9. Outline • Introduction • Related Work • A collection of detectors • Pyramid System • Pose-Index feature • Proposed Method • Experiments

10. 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.

11. A collection of detectors A zero-background classifier A one-background classifier Combination A three-background classifier A five-background classifier Hand

12. Outline • Introduction • Related Work • A collection of detectors • Pyramid System • Pose-Index feature • Proposed Method • Experiments

13. Pyramid System • Pose estimation at first stage. • Pose-dedicated classifier at second stage. Pose estimator Estimate 5 Five Classifier Hand Estimate 1 One Classifier Hand

14. 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. Three classifier Five classifier Zero classifier One classifier

15. Outline • Introduction • Related Work • A collection of detectors • Pyramid System • Pose-Index feature • Proposed Method • Experiments

16. Pose-Index feature • Allowing features to be parameterized with the pose. • Need exhaustive pose exploration in testing.

17. Pose-Index feature • Training Pose-Index Feature parameterized with the pose. Labeled Three Labeled Five Labeled Zero Labeled One

18. Pose-Index feature • Testing Feature parameterized by zero hand posture. Feature parameterized by one hand posture. Pose-index feature Feature parameterized by three hand posture. Feature parameterized by five hand posture. Hand

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

20. Outline • Introduction • Related Work • Proposed Method • Experiments

21. Outline • Introduction • Related Work • Proposed Method • Main Idea • Framework • Implementation Details • Experiments

22. 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.

23. Outline • Introduction • Related Work • Proposed Method • Main Idea • Framework • Implementation Details • Experiments

24. Framework Edge Detector Pose Estimator frame Final Detector Pose-Indexed Feature 0/1

25. Outline • Introduction • Related Work • Proposed Method • Main Idea • Framework • Implementation Details • Experiments

26. Implementation Details Edge Detector Pose Estimator frame Final Detector Pose-Indexed Feature 0/1

27. 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.

28. Implementation Details • Edge Detector 8 bins Input frame 1

29. Implementation Details Edge Detector Pose Estimator frame Final Detector Pose-Indexed Feature 0/1

30. Implementation Details • Pose Estimators • : Computes the dominate edge orientation in the window translated according to (u,v). 14 Pose Estimators

31. Implementation Details • Pose Estimators - 1st Pose Estimator Input frame 8 bins h1=0.08 h2=0.15 h3=0.12 h4=0.09 l=(u,v) h6=0.21 h5=0.06 h7=0.18 h8=0.11

32. Implementation Details • Pose Estimators - 2ndPose Estimator Input frame 8 bins h1=0.05 h2=0.12 h3=0.18 h4=0.02 l=(u,v) h6=0.16 h5=0.05 h7=0.32 h8=0.10

33. Implementation Details Edge Detector Pose Estimator frame Final Detector Pose-Indexed Feature 0/1

34. 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.

35. Implementation Details • Pose-Indexed Feature - For 1st pose estimator , Input frame 8 bins g1=0.06 g2=0.17 g3=0.18 g4=0.09 l=(u,v) g6=0.15 g5=0.04 g7=0.20 g8=0.11

36. Implementation Details • Pose-Indexed Feature - For 2nd pose estimator , Input frame 8 bins g1=0.03 g2=0.15 g3=0.16 g4=0.03 l=(u,v) g6=0.13 g5=0.04 g7=0.28 g8=0.17

37. Implementation Details Edge Detector Pose Estimator frame Final Detector Pose-Indexed Feature 0/1

38. Implementation Details • Final detector • Ex : AdaBoost Classifier

39. Outline • Introduction • Related Work • Proposed Method • Experiments

40. Outline • Introduction • Related Work • Proposed Method • Experiments • Aerial Images of Cars • Face Images • Hand Video Sequence

41. Experiments • Aerial Images of Cars

42. Outline • Introduction • Related Work • Proposed Method • Experiments • Aerial Images of Cars • Face Images • Hand Video Sequence

43. Experiments • Face Images

44. Outline • Introduction • Related Work • Proposed Method • Experiments • Aerial Images of Cars • Face Images • Hand Video Sequence

45. Experiments • Hand Video Sequence https://www.youtube.com/watch?v=NbeHYxRNtAw

46. Reference • “A Real-Time Deformable Detector,” Karim Ali, Franc¸oisFleuret, David Hasler, and Pascal Fua, IEEE Transactions on Pattern Analysis and Machine Intelligence 2012.