1. EE462 MLCV Lecture 5-6 Object Detection – Boosting Tae-Kyun Kim

2. EE462 MLCV Face Detection Demo 2 Robust real-time object detector, Viola and Jones, CVPR 01 Implemented by Intel OpenCV

3. EE462 MLCV 3 Multiclass object detection [Torralba et al PAMI 07] A boosting algorithm, originally for binary class problems, has been extended to multi-class problems.

4. EE462 MLCV Object Detection 4 Input is a single image, given without any prior knowledge.

5. EE462 MLCV Object Detection 5 Output is a set of tight bounding boxes (positions and scales) of instances of a target object class (e.g. pedestrian).

6. EE462 MLCV 6 Object Detection We scan every scale and pixel location of an image.

7. EE462 MLCV Number of windows 7 Number of Windows: 747,666 … x # of scales # of pixels It ends up with a huge number of candidate sub-windows.

8. EE462 MLCV Time per window 8 or raw pixels …… dimension D Num of feature vectors: 747,666 … Classification: What amount of time are we given to process a single scanning window? SIFT

9. EE462 MLCV Time per window 9 or raw pixels …… dimension D Num of feature vectors: 747,666 … Time per window (or vector): 0.00000134 sec In order to finish the task in 1 sec Neural Network? Nonlinear SVM?

10. EE462 MLCV Examples of face detection 10 From Viola, Jones, 2001

11. EE462 MLCV  By Integrating Visual Cues [Darrell et al IJCV 00].  Face pattern detection output (left).  Connected components recovered from stereo range data (mid).  Flesh hue regions from skin hue classification (right). 11 More traditionally… The search space is narrowed down

12. EE462 MLCV Since about 2001 (Viola &Jones 01)… “ Boosting Simple Features” has been a dominating art.  Adaboost classification  Weak classifiers: Haar-basis like functions (45,396 (>>T) in total feature pool) 12 Weak classifier Strong classifier

13. EE462 MLCV Introduction to Boosting Classifiers - AdaBoost (Adaptive Boosting)

14. EE462 MLCV Sorry for inconsistent notations… 14

15. EE462 MLCV Boosting 15

16. EE462 MLCV Boosting 16

17. EE462 MLCV Principally, Boosting does  Iteratively reweighting training samples,  Assigning higher weights to previously misclassified samples. 17 1 round2 rounds3 rounds4 rounds5 rounds50 rounds

18. EE462 MLCV 18 In the previous example, We consider all horizontal or vertical lines. For each line, we define two weaklearners. +1 +1

19. EE462 MLCV AdaBoost 19 M : # of weak classifiers to choose, among all weak classifiers,

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21. EE462 MLCV Boosting 21

22. EE462 MLCV Boosting as an optimisation framework 22

23. EE462 MLCV Minimising Exponential Error 23

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28. EE462 MLCV Existence of weak learners  Definition of a baseline learner  Data weights:  Set  Baseline classifier: for all x  Error is at most ½.  Each weak learner in Boosting is demanded s.t. → Error of the composite hypothesis goes to zero as boosting rounds increase [Duffy et al 00]. 28

29. EE462 MLCV Robust real-time object detector

30. EE462 MLCV Boosting Simple Features [Viola and Jones CVPR 01]  Adaboost classification  Weak classifiers: Haar-basis like functions (45,396 in total feature pool) 30 Weak classifier Strong classifier 20 ……

31. EE462 MLCV 31 Learning (concept illustration) Face images Non-face images Resize to 20x20 D=400 …… weaklearners Output:

32. EE462 MLCV Evaluation (testing) 32 From Viola, Jones, 2001 For given Non-local maxima suppression we apply the boosting classifier to every scan-window. Non-local maxima supression is performed.

33. EE462 MLCV How to accelerate boosting training and evaluation

34. EE462 MLCV Integral Image  A value at (x,y) is the sum of the pixel values above and to the left of (x,y).  The integral image can be computed in one pass over the original image. 34

35. EE462 MLCV Boosting Simple Features [Viola and Jones CVPR 01]  Integral image  The sum of original image values within the rectangle can be computed: Sum = A-B-C+D  This provides the fast evaluation of Haar-basis like features 35

36. EE462 MLCV Evaluation (testing) 36 From Viola, Jones, 2001 x y ii(x,y)

37. EE462 MLCV Boosting as a Tree-structured Classifier

38. EE462 MLCV Boosting (very shallow network)  The strong classifier H as boosted decision stumps has a flat structure  Cf. Decision “ferns” has been shown to outperform “trees” [Zisserman et al, 07] [Fua et al, 07] 38 c0 c1 x ……

39. EE462 MLCV Boosting -continued  Good generalisation is achieved by a flat structure.  It provides fast evaluation.  It does sequential optimisation. 39 A strong boosting classifier Boosting Cascade [viola & Jones 04], Boosting chain [Xiao et al]  It is very imbalanced tree structured.  It speeds up evaluation by rejecting easy negative samples at early stages.  It is hard to design A strong boosting classifier T = 2 5 10 20 50 100 ……

40. EE462 MLCV A cascade of classifiers  The detection system requires good detection rate and extremely low false positive rates.  False positive rate and detection rate are f_i is the false positive rate of i-th classifier on the examples that get through to it.  The expected number of features evaluated is p_j is the proportion of windows input to i-th classifier. 40

41. EE462 MLCV Demo video: Fast evaluation 41

42. EE462 MLCV Object Detection by a Cascade of Classifiers 42 Pictures from Romdhani et al. ICCV01 It speeds up object detection by coarse-to-fine search.