1. Class-Specific Hough Forests for Object Detection Zhen Yuan Hsu Advisor：S.J.Wang Gall, J., Lempitsky, V.: Class-specic hough forests for object detection. In: IEEE CVPR(2009)

2. Outline Related work Why we use Random forest What’s Hough forest How Hough forest work for object detection

3. Implicit shape models: Training Extract 25x25 patches around Harris corners. Generate a codebook of local appearance patches using clustering. For each cluster, extract its center and store it in the codebook. For each codebook entry, store all positions it was found relative to object center.

4. Implicit shape models: Testing 1.Given test image, extract patches, match to codebook entry 2.Cast votes for possible positions of object center 3.Search for maxima in voting space 4.Extract weighted segmentation mask based on stored masks for the codebook occurrences Match 、offset

5. Why we use Random forest Time、Training data Random forest

6. Decision tree x 1 >w 1 x 2 >w 2 Yes No x1x1 x2x2 W1W1 W2W2

7. A Forest …… tree t 1 tree t T category c split nodes leaf nodes v v

8. What’s Randomness Randomness – Data and Split fuction for each node： Split fuction is randomly selected.

9. Binary Tests selected during training from a random subset of all split functions. split node. P.q a threshold ： 16*16 image feature choice

10. Randomness - Split fuction Try several lines, chosen at random Keep line that best separates data –information gain Recurse

11. Random forest for object detection Object localization x：regression Classfying patch belong to object c：classification

12. What’s Hough forest Random forestHough vote Hough forest

13. Hough Forests ： Training Supervised learning Label ： negative or background samples (blue) positive samples (red) offset vectors (green) Feature of local patch

14. Hough Forests ： Training …… split nodes leaf nodes C L : positive sample patch proportion

15. Leaves two important information for voting: 1.C L : positive sample patch proportion 2. D L ={d i }, iA Stop criteria Leaf condition： 1. number of image patches < 2.a threshold based on minimum of uncertainty(Class-label, Offset vector)

16. Quality of Binary Tests Goal ： Minimize the Class-label uncertainty and Offset uncertainty: Type of uncertainty is randomly selected for each node Class-label uncertainty: Offset uncertainty: A=the set of all image patch={ } C i =class label

17. Detection Original image Interest points Matched patches Position y.

18. Detection …… Possible Center of objet：y+di 1.C L : positive sample patch proportion 2.D L ={d i } iA Position y.

19. Hough vote Probabilistic votes Source: B. Leibe Position y. d2d2 d1d1 d3d3

20. Hough vote For location x and given image patch I (y) and tree T x：center of bounding box x≈y+d i Confidence vote： 1.C L =weight 2. d i :offest vector Over all trees: Accumulation over all image patches:

21. Detection

22. Multi-Scale and Multi-Ratio Multi Scale: 3D Votes (x, y, scale) Multi-Ratio: 4D Votes (x, y, scale, ratio)

23. UIUC Cars - Multi Scale Wrong (EER) Correct

24. Comparison

25. Pedestrians (INRIA)

27. Pedestrians (TUD)

28. reference

29. Thank you for your listening!