Class-Specific Hough Forestsfor Object Detection

Zhen Yuan HsuAdvisor ： S.J.Wang

Gall, J., Lempitsky, V.: Class-specic hough forests for object detection. In: IEEE CVPR(2009)

Outline

• Related work• Why we use Random forest• What’s Hough forest• How Hough forest work for object detection

Implicit shape models: Training• 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: Testing1. Given test image, extract patches, match to codebook

entry 2. Cast votes for possible positions of object center3. Search for maxima in voting space4. Extract weighted segmentation mask based on stored

masks for the codebook occurrencesMatch 、 offset

Why we use Random forest

Time 、 Training data

Random forest

Decision tree

x1>w1

x2>w2

Yes

Yes

No

No

x1

x2

W1

W2

A Forest

……tree t1 tree tT

category ccategory c

split nodesleaf nodes

v v

What’s RandomnessRandomness – Data and Split fuction

for each node ：Split fuction is randomly selected.

Binary Tests

• selected during training from a random subset of all split functions.

split node

. P .q

a threshold

： 16*16 image feature

choice

Randomness - Split fuction• Try several lines,

chosen at random

• Keep line that best separates data– information gain

• Recurse

Random forest for object detection

Object localization x ： regression

Classfying patch belong to object c ：classification

datax

y

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

……

split nodesleaf nodes

CL : positive sample patch proportion

Leavestwo important information for voting:1.CL : positive sample patch proportion2. DL={di} , iϵA

Stop criteriaLeaf 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

Original imageInterest pointsMatched patches

Position y .

Detection

……

Possible Center of objet ： y+di

1.CL : positive sample patch proportion2.DL={di} iϵA

Position y .

Hough vote

Probabilistic votesSource: B. Leibe

Position y .

d2

d1d3

Hough voteFor location x and given image patch I(y) and tree T

x ： center of bounding box x≈y+di

• Confidence vote ： 1.CL =weight 2. di :offest vector

Over all trees:

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• 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!