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Histograms of Oriented Gradients forHuman Detection
Navneet Dalal and Bill Triggs
INRIA Rhone-Alpes
Grenoble, France
Funding: aceMedia, LAVA, Pascal Network
Histograms of Oriented Gradients for Human Detection – p. 1/13
Introduction
Detect & localize upright peoplein static images
ChallengesWide variety of articulated poses
Variable appearance/clothing
Complex backgrounds
Unconstrained illumination
Occlusions, different scales
ApplicationsPedestrian detection for smart cars
Film & media analysis
Visual surveillance
Histograms of Oriented Gradients for Human Detection – p. 2/13
Approach & Data Set
We focus on building robust feature sets
Classifier is just linear SVM on normalizedimage windows, is reliable & fast
Moving window based detector withnon-maximum suppression over scale-space
Data set availablehttp://pascal.inrialpes.fr/data/human/
Dat
aS
et
Train Test
614 positive images 288 positive images
1218 negative images 453 negative images
1208 positive windows 566 positive windows
Overall 1774 human annotations + reflections
Histograms of Oriented Gradients for Human Detection – p. 3/13
Feature Sets
Haar Wavelets + SVM: Papageorgiou & Poggio (2000), Mohan et al(2001), DePoortere et al (2002)
Rectangular differential features + adaBoost: Viola & Jones (2001)
Parts based binary orientation position histograms + adaBoost:Mikolajczyk et al (2004)
Edge templates + nearest neighbor: Gavrila & Philomen (1999)
Dynamic programming: Felzenszwalb & Huttenlocher (2000), Ioffe &Forsyth (1999)
Orientation histograms: c.f. Freeman et al (1996), Lowe (1999)
Other descriptors:- Shape contexts: Belongie et al (2002)- PCA-SIFT: Ke and Sukthankar (2004)
Histograms of Oriented Gradients for Human Detection – p. 4/13
Processing Chain
Orientation Voting
Overlapping Blocks
Local NormalizationInput Image Gradient Image
gradientsCompute into spatial &
Weighted vote
orientation cellscolour
Normalizegamma &
Inputimage
over detectionwindow
Collect HOG’s
SVMLinear
spatial blocksover overlappingContrast normalize
non−personPerson /
classification
Histograms of Oriented Gradients for Human Detection – p. 5/13
HOG Descriptors
Parameters Schemes
Gradient scale
Orientation bins
Percentage of block overlap
RGB or Lab, color/gray-space
Block normalization,L2-norm, v → v/
√
‖v‖2
2+ ε2
orL1-norm, v →
√
v/(‖v‖1 + ε)
R-HOG/SIFT C-HOG
Blo
ck
Cell
Blo
ck
Center Bin
Radial Bins, Angular Bins
Histograms of Oriented Gradients for Human Detection – p. 6/13
Performance
MIT pedestrian database INRIA person database
10−6
10−5
10−4
10−3
10−2
10−1
0.010.02
0.05
0.1
0.2DET − different descriptors on MIT database
false positives per window (FPPW)
mis
s ra
te
Lin. R−HOGLin. C−HOGLin. EC−HOGWaveletPCA−SIFTLin. G−ShaceCLin. E−ShaceCMIT best (part)MIT baseline
10−6
10−5
10−4
10−3
10−2
10−1
0.01
0.02
0.05
0.1
0.2
0.5DET − different descriptors on INRIA database
false positives per window (FPPW)m
iss
rate
Ker. R−HOGLin. R2−HOGLin. R−HOGLin. C−HOGLin. EC−HOGWaveletPCA−SIFTLin. G−ShapeCLin. E−ShapeC
- R/C-HOG give near perfect seperation on MIT database- Have 1-2 orders of magnitude lower false positives than otherdescriptors
Histograms of Oriented Gradients for Human Detection – p. 7/13
Gradient Smoothening & Orientation Bins
Gradient scale, σ Orientation bins, β
10−6
10−5
10−4
10−3
10−2
10−1
0.01
0.02
0.05
0.1
0.2
0.5DET − effect of gradient scale σ
false positives per window (FPPW)
mis
s ra
te
σ=0σ=0.5σ=1σ=2σ=3σ=0, c−cor
10−6
10−5
10−4
10−3
10−2
10−1
0.01
0.02
0.05
0.1
0.2
0.5DET − effect of number of orientation bins β
false positives per window (FPPW)m
iss
rate
bin= 9 (0−180)bin= 6 (0−180)bin= 4 (0−180)bin= 3 (0−180)bin=18 (0−360)bin=12 (0−360)bin= 8 (0−360)bin= 6 (0−360)
Using simple smoothed gradients & many orientations helps!
Reducing gradient scale from 3 to 0 decreases false positives by10 times
Increasing orientation bins from 4 to 9 decreases false positives by10 times
Histograms of Oriented Gradients for Human Detection – p. 8/13
Normalization Method & Block Overlap
Normalization method Block overlap
10−5
10−4
10−3
10−2
0.02
0.05
0.1
0.2DET − effect of normalization methods
false positives per window (FPPW)
mis
s ra
te
L2−HysL2−normL1−SqrtL1−normNo normWindow norm
10−6
10−5
10−4
10−3
10−2
10−1
0.01
0.02
0.05
0.1
0.2
0.5DET − effect of overlap (cell size=8, num cell = 2x2, wt=0)
false positives per window (FPPW)
mis
s ra
te
overlap = 3/4, stride = 4overlap = 1/2, stride = 8overlap = 0, stride =16
Strong local normalization isessential
Overlapping blocks improvesperformance, but descriptor sizeincreases
Histograms of Oriented Gradients for Human Detection – p. 9/13
Effect of Block & Cell Size
4x46x6
8x810x10
12x12
Cell size (pixels)
1x12x2
3x34x4 Block size (Cells)
0
5
10
15
20
Mis
s R
ate
(%)
Trade off between need for local spatial invarianceand need for finer spatial resolution
Histograms of Oriented Gradients for Human Detection – p. 10/13
Descriptor Cues
input image weightedpos wts
weightedneg wts
avg. grad outside in block
The most important cuesare head, shoulder, legsilhouettes
Vertical gradients insidethe person count asnegative
Overlapping blocks thosejust outside the contourare the most important
Histograms of Oriented Gradients for Human Detection – p. 11/13
Conclusions
Fine grained features improve performanceNo gradient smoothning, [−1, 0, 1] derivative mask
Use gradient magnitude (no thresholding)
Orientation voting into fine bins
Spatial voting into coarser bins
Strong local normalization
Overlapping normalization blocks
- A general object classifier- Also works well for other classes- Linear SVM is reliable & fast, but not optimal- Human detection: 90% at 10−4 false positives per window
Histograms of Oriented Gradients for Human Detection – p. 12/13
Demo
No temporal smoothning
Histograms of Oriented Gradients for Human Detection – p. 13/13