1. Viola/Jones: features “Rectangle filters” Differences between sums of pixels in adjacent rectangles { y t (x) = +1 if h t (x) >  t -1 otherwise Unique Features { Detection = face, if Y(x) > 0 non-face, otherwise Y(x)=∑α t y t (x) Robust Realtime Face Dection, IJCV 2004, Viola and Jonce Select 200 by Adaboost

2. Integral Image (aka. summed area table) Define the Integral Image Any rectangular sum can be computed in constant time: Rectangle features can be computed as differences between rectangles

3. Feature selection (AdaBoost) Given training data {x n,t n }, find {α t } for {y t (x)} by minimizing total error function: Ideal function error(z) = z>0?0:1, hard to optimize. Instead use error(z)=exp(-z) to make the optimization convex. Define Basic idea: first find f 1 (x) by minimizing E(f 1 ) Then given f m-1 (x), find f m (x) by searching for best α m and y m (x)

4. Feature selection (AdaBoost) w n (m) =exp(-t n f m-1 (x n )) is high if f m-1 (x) is correct for x n ; is low otherwise. Next we want to find α m and y m (x) to minimize this weighted error function

5. Feature selection (AdaBoost) Recall t n in {1,+1} and y m (x) in {-1,+1}

6. Feature selection (AdaBoost) Find y m (x) to minimize Find α m to minimize Calculate weighted error rate for y m (x)

7. Feature selection (AdaBoost) Update weight w n (m+1) =exp(-t n f m (x n )) Note Only need to update weight for incorrectly classified data

8. Viola/Jones: handling scale Smallest Scale Larger Scale 50,000 Locations/Scales

9. Cascaded Classifier 1 Feature 5 Features F 50% 20 Features 20%2% FACE NON-FACE F F IMAGE SUB-WINDOW first classifier: 100% detection, 50% false positives. second classifier: 100% detection, 40% false positives (20% cumulative) using data from previous stage. third classifier: 100% detection,10% false positive rate (2% cumulative) Put cheaper classifiers up front

10. Viola/Jones results: Run-time: 15fps (384x288 pixel image on a 700 Mhz Pentium III)

11. Application Smart cameras: auto focus, red eye removal, auto color correction

12. Application Lexus LS600 Driver Monitor System

13. Pedestrian Detection: Chamfer matching Gavrila & Philomin ICCV 1999 Best Match Distance Transform TemplateEdge DetectionInput Image Slides from K. Grauman and B. Leibe

14. Pedestrian Detection: Chamfer matching Hierarchy of templates Gavrila & Philomin ICCV 1999 Slides from K. Grauman and B. Leibe

15. Pedestrian Detection: HOG Feature Slides from Andrew Zisserman

16. Pedestrian Detection: HOG Feature Dalal & Triggs, CVPR 2005 Slides from Andrew Zisserman HOG: Histogram of Gradients

17. Pedestrian Detection: HOG Feature Dalal & Triggs, CVPR 2005 Map each grid cell in the input window to a gradient-orientation histogram weighted by gradient magnitude Code: http://pascal.inrialpes.fr/soft/olt Slides from K. Grauman and B. Leibe

18. Pedestrian Detection: HOG Feature Slides from Andrew Zisserman

19. Pedestrian Detection: HOG Feature Slides from Andrew Zisserman

20. Algorithm Slides from Andrew Zisserman

21. Model training using SVM Given Find To minimize

22. Result

23. Learned model Slides from Deva Ramanan

24. Meaning of negative weights wx>-b (w + -w - )x>-b w + x-w - x>-b Slides from Deva Ramanan Complete model should compete pedestrian/pillar/doorway

25. Faces and Pedestrians Relatively easier, but can still be confusing Slide credit: Lana Lazebnik

26. More difficult cases

27. In general classify every pixel