Class-Specific Hough Forests
for Object Detection
Zhen Yuan Hsu
Advisor：S.J.Wang
Gall, J., Lempitsky, V.: Class-specic hough forests for object detection. In: IEEE CVPR(2009)
Outline
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Related work
Why we use Random forest
What’s Hough forest
How Hough forest work for object detection
Implicit shape models: Training
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Extract 25x25 patches around Harris corners.
Generate a codebook of local appearance patches using clustering.
For each cluster, extract its center and store it in the codebook.
For each codebook entry, store all positions it was found relative to
object center.
Implicit shape models: Testing
1.
2.
3.
4.
Given test image, extract patches, match to codebook entry
Cast votes for possible positions of object center
Search for maxima in voting space
Extract weighted segmentation mask based on stored masks for
the codebook occurrences
Match 、offset
Why we use Random forest
Random forestdata
Time、Training
Decision tree
x2
x1>w1
Yes
No
W2
x2>w2
Yes
No
W1
x1
A Forest
v
leaf nodes
split nodes
v
tree t1
……
category c
category c
tree tT
What’s Randomness
Randomness – Data and Split fuction
for each node：
Split fuction is randomly selected.
Binary Tests
split node
.P
：16*16 image feature
.q
a threshold
choice
• selected during training from a random subset of all split functions.
Randomness - Split fuction
• Try several lines,
chosen at random
• Keep line that best
separates data
– information gain
• Recurse
Random forest for object detection
y
Object localization x：regression
x
data
Classfying patch belong to object c：classification
What’s Hough forest
Random forest
Hough vote
Hough forest
Hough Forests：Training
• Supervised learning
• Label：
negative or background samples (blue)
positive samples (red)
offset vectors (green)
Feature of local patch
Hough Forests：Training
leaf nodes
split nodes
……
CL : positive sample patch proportion
Leaves
two important information for voting:
1.CL : positive sample patch proportion
2. DL={di} , iϵA
Stop criteria
Leaf condition：
1. number of image patches < ϵ
2.a threshold based on minimum of uncertainty(Class-label ,
Offset vector)
Quality of Binary Tests
• Goal：
Minimize the Class-label uncertainty and Offset uncertainty:
• Type of uncertainty is randomly selected for each node
• Class-label uncertainty:
• Offset uncertainty:
A=the set of all image patch={
Ci=class label
}
Detection
Position y
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Original
image
Interest
points
Matched
patches
Detection
Position y
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……
1.CL : positive sample patch proportion
2.DL={di} iϵA
Possible Center of objet：y+di
Hough vote
d2
d1
Position y
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d3
Probabilistic votes
Source: B. Leibe
Hough vote
For location x and given image patch I(y) and tree T
x：center of bounding box
x≈y+di
Over all trees:
• Confidence vote：
1.CL =weight
2. di :offest vector
Accumulation over all image patches:
Detection
Multi-Scale and Multi-Ratio
• Multi Scale: 3D Votes (x, y, scale)
• Multi-Ratio: 4D Votes (x, y, scale, ratio)
UIUC Cars - Multi Scale
• Wrong (EER)
• Correct
Comparison
Pedestrians (INRIA)
Pedestrians (INRIA)
Pedestrians (TUD)
reference
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http://mi.eng.cam.ac.uk/~tkk22/iccv09_tutorial
利用霍夫森林建構行人偵測技術- 清華電機系 陳仕儒碩士論文2012
An Introduction to Random Forests for Multi-class Object Detection, J.Gall
• Thank you for your listening!