Our ICIP 2010 oral presentation

1. 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
2012-09-27 1

2. Outline
Introduction
Background subtraction based on phase
feature and distance transform
Experimental results
Conclusion
2012-09-27 2

3. Introduction (1)
Moving object detection is an active
research subject in computer vision.
Foreground detection under sudden
illumination change is still a challenging
problem.
2012-09-27 3

4. 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
2012-09-27 4

5. 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
2012-09-27 5

6. Motivation
Phase is insensitive to illumination
change.
The basic framework of GMM method.
Blob aggregation using distance
transform.
2012-09-27 6

7. Diagram
Background subtraction
based on phase feature
and distance transform
2012-09-27 7

8. Phase feature extraction (1)
The Gabor wavelet coefficient:
G , ( z )  A , z ( z )  exp(i  ,v ( z ))
A ,v ( z ) :amplitude item
  , ( z ) [0,2 ) :phase item
Each pixel has max  max wavelet coefficients
2012-09-27 8

9. 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
2012-09-27 9

10. Phase feature extraction (3)
The phase feature is defined:
L
p( z )   i ( z )
i 1
p(z ) : the phase feature
 i (z ) : the Gabor phase corresponding to the ith largest Gabor
amplitude of the pixel
L : the number of phase to be extracted
N  N image subdivision
2012-09-27 10

11. Background modeling
K mixture of Gaussian distributions
In the background modeling stage, each pixel is modeled
independently by a mixture of Gaussian distributions
where each Gaussian represents the phase distribution.
kth Gaussian distribution represented by
 k ,  and  k , total weights   i  1
K
2
k
i 1
2012-09-27 11

12. Model updating (1)
Set L  3, p( z ) [0,6 )
Singular regions exists, matching condition
is in different expressions
 6  k  X t  2.5 k if X t   and 6  k  

 6  k  X t  2.5 k if  k   and 6  X t  
 X    2.5 others
 t k k
2012-09-27 12

13. Model updating (2)
Condition satisfied
1. if  k   and 6  X t  
  k   k   ( X t   k  6 )
 2
 k  (1   ) k2   ( X t   k  6 ) 2
2. if X t   and 6  k  
  k   k   ( X t   k  6 )
 2
 k  (1   ) k2   ( X t   k  6 ) 2
3. Others
 k  k   ( X t  k )
 2
 k  (1   ) k2   ( X t   k ) 2
2012-09-27 13

14. Model updating (3)
Weights update k  (1   )k  M k
 k is beyond [0,6 )
 k   k  6 if  k  6

 k   k  6 if  k  0
Condition not satisfied
New Gaussian replace   X t ,    init , low init
2012-09-27 14

15. Foreground detection
Sorted in  
Background distributions:
b
B  arg min( k  T )
k 1
2012-09-27 15

16. Distance transform (1)
Initial detection result
2012-09-27 16

17. Distance transform (2)
Euclidean distance transform of pixel a(i, j )
d (i, j )  min (i  x) 2  ( j  y) 2
( x , y )W
2012-09-27 17

18. Blob aggregation
Example results before and after blob aggregation
2012-09-27 18

19. 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.
TP TP
Precision   100% Recall   100%
TP  FP TP  FN
2012-09-27 19

20. Experiments (2)
Parameter selection
Gabor filter:  max  4 max  6
Phase extraction: N  4, L  3
Gaussian model: K  3, Tb  0.7
Segment value for DT: Td  1.2
2012-09-27 20

21. Experiments (3)
（a） （b） （c）
（d） （e） （f）
GMM LBP Our
2012-09-27 21

22. Experiments (4)
Precision and recall evaluation
Precision Recall
GMM 22.03 76.40
LBP 18.40 73.05
Our method 78.53 97.48
2012-09-27 22

23. Experiments (5)
（a） （b） （c）
（d） （e） （f）
GMM LBP Our
2012-09-27 23

24. Experiments (6)
Precision and recall evaluation
Precision Recall
GMM 4.52 26.01
LBP 14.27 46.91
Our method 73.58 92.25
2012-09-27 24

25. Conclusions
Pixel phase as feature.
Novel matching condition and updating
scheme.
Distance transform on the initial
detection results.
2012-09-27 25