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.886 - 893   Presenter :JIA-HONG,DONG Advisor : Yen- Ting, Chen

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

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

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

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

Methodology Data Sets MIT pedestrian database INRIA 509 training images 200 test images INRIA 1805 64X128 images

Methodology 1 2 3 4 5 6 7 8 9 10

Methodology Training examples 12180+ examples 2478 Positive 1218 Negative

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

Methodology

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

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

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)

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

Power Law (Gamma equalization)

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

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

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

Spatial / Orientation Binning

Normalization and Descriptor Blocks

Normalization and Descriptor Blocks

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

Normalization and Descriptor Blocks

Normalization and Descriptor Blocks Illumination and foreground-background contrast overlap

Normalization and Descriptor Blocks

Detector Window and Context

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

Classifier Using a Gaussian kernel SVM,

Results

Results

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

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

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

Thank you for your attention