Viola/Jones: features

“Rectangle filters”

Differences between sums of pixels in adjacent rectangles

{yt(x) =+1 if ht(x) > t

-1 otherwise

000,000,6100000,60 Unique Features

{Detection =face, if Y(x) > 0non-face, otherwise

Y(x)=∑αtyt(x)

Robust Realtime Face Dection, IJCV 2004, Viola and Jonce

Select 200 by Adaboost

Integral Image (aka. summed area table)

• Define the Integral Image

• Any rectangular sum can be computed in constant time:

• Rectangle features can be computed as differences between rectangles

yyxx

yxIyxI

''

)','(),('

D

BACADCBAA

D

)()(

)32(41

Feature selection (AdaBoost)

Given training data {xn,tn}, find {αt} for {yt(x)} by minimizing total error function:

Ideal function error(z) = z>0?0:1, hard to optimize.Instead use error(z)=exp(-z) to make the optimization convex.

Define

Basic idea: first find f1(x) by minimizing E(f1)Then given fm-1(x), find fm(x) by searching for best αm and ym(x)

Feature selection (AdaBoost)

wn(m)=exp(-tnfm-1(xn)) is high if fm-1(x) is correct for xn; is low

otherwise.Next we want to find αm and ym(x) to minimize this weighted error function

Feature selection (AdaBoost)

Recall tn in {1,+1} and ym(x) in {-1,+1}

Feature selection (AdaBoost)

Find ym(x) to minimize

Find αm to minimize

Calculate weighted error rate for ym(x)

Feature selection (AdaBoost)

Update weight wn(m+1)=exp(-tnfm (xn))

Note

Only need to update weight for incorrectly classified data

Viola/Jones: handling scale

SmallestScale

LargerScale

50,000 Locations/Scales

Cascaded Classifier

1 Feature 5 Features

F

50%20 Features

20% 2%

FACE

NON-FACE

F

NON-FACE

F

NON-FACE

IMAGESUB-WINDOW

• first classifier: 100% detection, 50% false positives.• second classifier: 100% detection, 40% false positives • (20% cumulative)

• using data from previous stage. • third classifier: 100% detection,10% false positive rate • (2% cumulative)

• Put cheaper classifiers up front

Viola/Jones results:

Run-time: 15fps (384x288 pixel image on a 700 Mhz Pentium III)

Application

Smart cameras: auto focus, red eye removal, auto color correction

Application

Lexus LS600 Driver Monitor System

Pedestrian Detection: Chamfer matching

Gavrila & Philomin ICCV 1999

Best Match

Distance Transform

TemplateEdge DetectionInput Image

Slides from K. Grauman and B. Leibe

Pedestrian Detection: Chamfer matching

Hierarchy of templates

Gavrila & Philomin ICCV 1999Slides from K. Grauman and B. Leibe

Pedestrian Detection: HOG Feature

Slides from Andrew Zisserman

Pedestrian Detection: HOG Feature

Dalal & Triggs, CVPR 2005 Slides from Andrew Zisserman

HOG: Histogram of Gradients

Pedestrian Detection: HOG Feature

Dalal & Triggs, CVPR 2005

Map each grid cell in the input window to a gradient-orientation histogram weighted by gradient magnitudeCode: http://pascal.inrialpes.fr/soft/olt

Slides from K. Grauman and B. Leibe

Pedestrian Detection: HOG Feature

Slides from Andrew Zisserman

Pedestrian Detection: HOG Feature

Slides from Andrew Zisserman

Algorithm

Slides from Andrew Zisserman

Model training using SVM• Given

• Find

• To minimize

Result

Learned model

Slides from Deva Ramanan

Meaning of negative weights

wx>-b(w+-w-)x>-bw+x-w-x>-b

Slides from Deva Ramanan

Complete model should compete pedestrian/pillar/doorway

Faces and Pedestrians• Relatively easier, but can still be confusing

Slide credit: Lana Lazebnik

More difficult cases

In general• classify every pixel