Post on 13-Dec-2015
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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