1. Histograms of Oriented Gradients for Human Detection(HOG)
Dalal, N.; Triggs, B., IEEE Computer Society Conference on Computer Vision and Pattern Recognition(2005) vol. 1 ,pp  
Presenter :JIA-HONG,DONG
Advisor : Yen- Ting, Chen

2. Outline
1. Introduction 2. Methodology 3. Results 4. Discussion 5. Conclusion

3. Introduction Detecting humans in images is a challenging task
Variable appearance
Wide range of poses
A robust feature set
Discriminate cleanly
Cluttered backgrounds
Different illumination

4. Introduction Edge orientation histograms
Scale-invariant feature transform (SIFT)
Shape context
SIFT
Shape context

5. Introduction Using linear SVM as a baseline classifier
Using detection error tradeoff (DET)
Data Sets
MIT pedestrian set
INRIA pedestrian set

6. Methodology Data Sets MIT pedestrian database INRIA
509 training images
200 test images
INRIA
X128 images

7. Methodology 1 2 3 4 5 6 7 8 9 10

8. Methodology Training examples 12180+ examples 2478 Positive
1218 Negative

9. Methodology Detection error tradeoff X-axes Y-axes Log-log scale
False Positives Per Window tested(by 5% at 10-4)
FPPW=
Y-axes
Miss rate=
Log-log scale

10. Methodology

11. Gamma/Color Normalization
Inputting pixel representations
Grayscale
RGB color spaces
LAB color spaces
Power law (Gamma equalization)

12. LAB Color Spaces Xn, Yn and Zn are the CIE XYZ tristimulus values
of the reference white point

13. Power Law (Gamma equalization)
Tradition
IGray(i, j) is the gray-level image
IEq (i, j) is the image which performed equalization
IMax and IMin are the maximum and minimum of the pixel values of IGray(i, j)

14. Power Law (Gamma equalization)
i is the i-th gray level
L is the low-bound
R is the actual equalization range
GE (i) is the result of the i-th gray level obtained from gamma equalization

15. Power Law (Gamma equalization)

16. Gradient Computation Masks test(for each color channel)
Gaussian (σ=0~3)
1-D point derivatives[-1,0,1]
Cubic-corrected[1,-8,0,8,-1]
3X3 Sobel mask
2X2 diagonal ones

17. Gradient Computation ‘c-cor’ is the 1D cubic-corrected
point derivative

18. Spatial / Orientation Binning
Orientation bins are evenly spaced
0 °~180 °
0 °~360 °

19. Spatial / Orientation Binning

20. Normalization and Descriptor Blocks

21. Normalization and Descriptor Blocks

22. Normalization and Descriptor Blocks
Block Normalization schemes
(limiting the maximum values of v to 0.2) and renormalizing
Centre-surround normalization
Window norm(using Gaussian σ=1)
v is the unnormalized descriptor vector
is a small constant

23. Normalization and Descriptor Blocks

24. Normalization and Descriptor Blocks
Illumination and foreground-background contrast
overlap

25. Normalization and Descriptor Blocks

26. Detector Window and Context

27. Classifier Using linear SVM(Support vector machine)
Increasing performance
Using a Gaussian kernel
Higher run time

28. Classifier
Using a Gaussian kernel SVM,

29. Results

30. Results

31. Results
The performance of selected detectors on (left) MIT and (right) INRIA data sets.

32. Discussion HOG outperform wavelet & shape context
Traditional centre-surround style schemes are not the best choice
Similar to SIFT descriptors

33. Conclusion Scale gradients Orientation binning
Relatively coarse spatial binning
High-quality local contrast normalization in overlapping descriptor blocks

34. Thank you for your attention