Histograms of Oriented Gradients for Human Detection(HOG)

Presenter :JIA-HONG,DONG

Advisor : Yen- Ting, Chen

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Dalal, N.; Triggs, B., IEEE Computer Society Conference on Computer Vision and Pattern Recognition(2005) vol. 1 ,pp.886 - 893  

Outline1. Introduction2. Methodology3. Results4. Discussion 5. Conclusion

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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

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Introduction Edge orientation histograms

Scale-invariant feature transform (SIFT) Shape context

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Shape contextSIFT

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

MIT pedestrian set INRIA pedestrian set

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Methodology Data Sets

MIT pedestrian database 509 training images 200 test images

INRIA 1805 64X128 images

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Methodology

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1 2 3 4 5

6 7 8 9 10

Methodology Training examples 12180+ examples

2478 Positive 1218 Negative

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Methodology Detection error tradeoff

X-axes False Positives Per Window tested(by 5% at 10-4) FPPW=

Y-axes Miss rate=

Log-log scale

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Methodology

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Gamma/Color Normalization Inputting pixel representations

Grayscale RGB color spaces LAB color spaces

Power law (Gamma equalization)

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LAB Color Spaces

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

Power Law (Gamma equalization) Tradition

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IGray(i, j) is the gray-level imageIEq (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)

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i is the i-th gray levelL is the low-boundR is the actual equalization rangeGE (i) is the result of the i-th gray level obtained from gamma equalization

Power Law (Gamma equalization)

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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

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Gradient Computation

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‘c-cor’ is the 1D cubic-correctedpoint derivative

Spatial / Orientation Binning Orientation bins are evenly spaced

0 °~180 ° 0 °~360 °

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Spatial / Orientation Binning

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Normalization and Descriptor Blocks

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Normalization and Descriptor Blocks

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Normalization and Descriptor Blocks

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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

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Normalization and Descriptor Blocks

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Illumination and foreground-background contrast overlap

Normalization and Descriptor Blocks

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Detector Window and Context

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Classifier Using linear SVM(Support vector machine) Increasing performance

Using a Gaussian kernel Higher run time

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Classifier

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Using a Gaussian kernel SVM,

Results

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Results

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Results

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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

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

overlapping descriptor blocks

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Thank you for your attention

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