1. Presented by Minh Hoai Nguyen Date: 28 March 2007
Object detection
Presented by Minh Hoai Nguyen
Date: 28 March 2007

2. Object detection? Challenges: + Diff locations + Diff Scales
+ Diff poses, expressions
+ Diff illuminations, skin color, glasses, occluded, reflection etc.

3. What we want
Miss a face!

4. Happy face!

5. Scanning window Train a classifier on a fixed size window
Seems to be slow but:
+ does work
+ can speed up using some tricks
Disadvantage:
+ No context information.
Advantage:
+ Only need to train classifier on a small, fixed-size window.

6. Outline Object Detection Using the Statistics of Parts
Schneiderman, H. & Kanade, T. CVPR00, IJCCV04
Robust Real-time Face Detection
Viola, P. & Jones, M. CVPR01, IJCV04

7. Bayes optimal classifier
Image is defined by n attrs: x1,x2,…,xn
There are too many parameters to learn

8. Naïve Bayes Assumption
Assume: x1,x2,…,xn are cond. independent.
Easier to learn
Problem: this might be a bad assumption
Idea:
Carefully divide x1,x2,…,xn into groups: P1, P2,…, Pk
Assume P1, P2,…, Pk are independent

9. Independent groups/parts
How to divide x1,x2,…,xn into ind. groups?
Image pixels are highly correlated.
Represent image by Wavelets instead.

10. 10 filter responses for each original pixel.
Wavelet transform HL
10 filter responses for each original pixel. HH LH
Wavelet transform is fully invertible.
Partially de-correlate natural imagery
More independence, easier to design parts

11. Designing parts Assumption: Parts:
Each wavelet coefficient only depends on few others.
Group those coefficients into parts.
Parts:
17 types, manually defined.
Each part contains 8 coefficients.

12. Slide credit: Nicholas Chan
Categories of parts
Intra-subband
Local operator
Inter-frequency
Local operator
“Parts”
Inter-orientation
Local operator
Inter-frequency/ Inter-orientation
Local operator
Slide credit: Nicholas Chan

13. How to compute these statistics?
Final form of detector
How to compute these statistics?
Count!

14. Multiple poses?
Other tricks:
Not going to talk about.

15. Reported results for faces
Kodak dataset:
Test set: 17 images, 46 faces, 36 profile views.

16. A bigger dataset
From multiple sources 208 images, 441 faces, about 347 profiles.

17. Robust Real-time Face Detection by Viola,P. & Jones, M.

18. Cascade of classifiers
Most places do not have faces!

19. Simple features Box filters Approximation of Harr-wavelets
Integral image
Feature evaluation can be done by few lookups

20. Learning the cascade
AdaBoost
Weak classifiers are box filters

21. Learning cascade stages
Using AdaBoost to train each stage:
Adjust threshold to minimize false negatives.
Adding features until target detection and false positive rates are met (determined by CV)

22. Learned cascade First classifier: 2 features 100% detection
40% false detection
The whole cascade:
38 stages
6000 features in total
On dataset with 507 faces and 75 millions sub-windows, faces are detected using 10 feature evaluations on average.
On average, 10 feature evals/sub-window

23. Reported ROC curve

24. Comparison results

25. The end