Background Subtraction Based on Phase and Distance

Transform Under Sudden Illumination Change

Authors: Gengjian Xue, Jun Sun, Li Song

Reporter: Li Song

Shanghai Jiao Tong University

1 2012-09-27

Outline

Introduction

Background subtraction based on phase

feature and distance transform

Experimental results

Conclusion

2 2012-09-27

Introduction (1)

Moving object detection is an active

research subject in computer vision.

Foreground detection under sudden

illumination change is still a challenging

problem.

3 2012-09-27

Introduction (2)

Parametric density estimation technique

Gaussian Mixture Models (GMM), C.Stauffer and

Grimson, CVPR, 1999.

Nonparametric density estimation technique

Kernel Density Estimation (KDE), A. Elgammal,

etc., Pro.IEEE, 2002.

Region-based technique

Local Binary Pattern based (LBP), M.Heikkila and

M. Pietikainen, TPAMI, 2006

4 2012-09-27

Introduction (3)

Other methods

Wallflower system, K.Toyama, etc., ICCV, 1999

Pixel order information based, Binglong Xie, etc.,

Image Vision Computing, 2004

Pilet’s method, ECCV 2008.

---a statistical model combing two texture-based

features and the ratios of the current image to the

background image in each color channel

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Motivation

Phase is insensitive to illumination

change.

The basic framework of GMM method.

Blob aggregation using distance

transform.

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Diagram

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Background subtraction

based on phase feature

and distance transform

Phase feature extraction (1)

The Gabor wavelet coefficient:

:amplitude item

:phase item

Each pixel has wavelet coefficients

8 2012-09-27

))(exp()()( ,,, zizAzG vz

)(, zA v

)2,0[)(, z

maxmax

Phase feature extraction (2)

Phase extraction principle:

The larger amplitude of the coefficient is, the

more efficient the coefficient represents the pixel

information and the more discriminative of the

corresponding phase is.

Multiple phase extraction reasons:

rotation property

range of single phase is small

9 2012-09-27

Phase feature extraction (3)

The phase feature is defined:

: the phase feature

: the Gabor phase corresponding to the largest Gabor

amplitude of the pixel

L : the number of phase to be extracted

image subdivision

10 2012-09-27

L

i

i zzp1

)()(

)(zp

)(zi thi

NN

Background modeling

mixture of Gaussian distributions

Gaussian distribution represented by

, and , total weights

11 2012-09-27

thk

k2

k

K

i

i

1

1k

K

In the background modeling stage, each pixel is modeled

independently by a mixture of Gaussian distributions

where each Gaussian represents the phase distribution.

Model updating (1)

Set ,

Singular regions exists, matching condition

is in different expressions

if and

if and

others

12 2012-09-27

)6,0[)( zp3L

kkt

ktk

ktk

X

X

X

5.2

5.26

5.26 tX k6

k tX6

Model updating (2)

Condition satisfied

1. if and

2. if and

3. Others

13 2012-09-27

k tX6

222 )6()1(

)6(

ktkk

ktkk

X

X

tX k6

222 )6()1(

)6(

ktkk

ktkk

X

X

222 )()1(

)(

ktkk

ktkk

X

X

Model updating (3)

Weights update

is beyond

if

if

Condition not satisfied

New Gaussian replace , low

14 2012-09-27

inittX , init

kkk M )1(

k )6,0[

6

6

kk

kk

0

6

k

k

Foreground detection

Sorted in

Background distributions:

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)min(arg1

TBb

kk

Distance transform (1)

Initial detection result

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Distance transform (2)

Euclidean distance transform of pixel

17 2012-09-27

22

),()()(min),( yjxijid

Wyx

),( jia

Blob aggregation

Example results before and after blob aggregation

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Experiments (1)

Several sequences for testing.

GMM and LBP methods are employed for

comparison.

Visual and numerical evaluation are used

Precision and Recall as the metrics.

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100%FPTP

TPPrecision

100%

FNTP

TPRecall

Experiments (2)

Parameter selection

Gabor filter:

Phase extraction:

Gaussian model:

Segment value for DT:

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3,4 LN

4max 6max

7.0,3 bTK

2.1dT

Experiments (3)

21 2012-09-27

（a） （b） （c）

（d）

GMM

（e）

LBP

（f）

Our

Experiments (4)

Precision Recall

GMM 22.03 76.40

LBP 18.40 73.05

Our method 78.53 97.48

22 2012-09-27

Precision and recall evaluation

Experiments (5)

23 2012-09-27

（a） （b） （c）

（d）

GMM

（e）

LBP

（f）

Our

Experiments (6)

Precision Recall GMM 4.52 26.01 LBP 14.27 46.91

Our method 73.58 92.25

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Precision and recall evaluation

Conclusions

Pixel phase as feature.

Novel matching condition and updating

scheme.

Distance transform on the initial

detection results.

25 2012-09-27