Abstract

The codebook background subtraction approach is widely used in computer vision applications. One of its distinguished features is the cylinder color model used to cope with illumination changes. The performances of this approach depends strongly on the color model. However, we have found this color model is valid only if the spectrum components of the light source change in the same proportion. In fact, this is not true in many practical cases. In these cases, the performances of the approach would be degraded significantly. To tackle this problem, we propose an arbitrary cylinder color model with a highly efficient updating strategy. This model uses cylinders whose axes need not going through the origin, so that the cylinder color model is extended to much more general cases. Experimental results show that, with no loss of real-time performance, the proposed model reduces the wrong classification rate of the cylinder color model by more than fifty percent.

© 2014 Optical Society of America

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References

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  1. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2004), pp. 3061–3064.
  2. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
    [CrossRef]
  3. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
    [CrossRef]
  4. L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
    [CrossRef] [PubMed]
  5. D. S. Lee, “Effective Gaussian mixture learning for video background subtraction,” IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005).
    [CrossRef] [PubMed]
  6. Z. Zivkovic and F. Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognit. Lett. 27(7), 773–780 (2006).
    [CrossRef]
  7. M. Heikklä and M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006).
    [CrossRef] [PubMed]
  8. L. Maddalena and A. Petrosino, “A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process. 17(7), 1168–1177 (2008).
    [CrossRef] [PubMed]
  9. D. M. Tsai and S. C. Lai, “Independent component analysis-based background subtraction for indoor surveillance,” IEEE Trans. Image Process. 18(1), 158–167 (2009).
    [CrossRef] [PubMed]
  10. O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Trans. Image Process. 20(6), 1709–1724 (2011).
    [CrossRef] [PubMed]
  11. S. Kwak, G. Bae, and H. Byun, “Moving-object segmentation using a foreground history map,” J. Opt. Soc. Am. A 27(2), 180–187 (2010).
    [CrossRef] [PubMed]
  12. C. Cuevas, R. Mohedano, and N. García, “Adaptable Bayesian classifier for spatiotemporal nonparametric moving object detection strategies,” Opt. Lett. 37(15), 3159–3161 (2012).
    [CrossRef] [PubMed]
  13. A. Elkabetz and Y. Yitzhaky, “Background modeling for moving object detection in long-distance imaging through turbulent medium,” Appl. Opt. 53(6), 1132–1141 (2014).
    [CrossRef] [PubMed]
  14. Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
    [CrossRef]
  15. M. H. Sigari and M. Fathy, “Real-time background modeling/subtraction using two-layer codebook model,” in Proceedings of the International Multi-Conference of Engineers and Computer Scientists (IAENG, 2008), pp. 717–720.
  16. A. Ilyas, M. Scuturici, and S. Miguet, “Real time foreground-background segmentation using a modified codebook model,” in Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (IEEE, 2009), pp. 454–459.
    [CrossRef]
  17. M. J. Wu and X. R. Peng, “Spatio-temporal context for codebook-based dynamic background subtraction,” AEU, Int. J. Electron. Commun. 64(8), 739–747 (2010).
    [CrossRef]
  18. J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
    [CrossRef]
  19. I. T. Sun, S. C. Hsu, and C. L. Huang, “A hybrid codebook background model for background subtraction,” in Workshop of IEEE Conference on Signal Processing Systems (IEEE, 2011), pp. 96–101.
    [CrossRef]
  20. M. Shah, J. Deng, and B. Woodford, “Enhanced codebook model for real-time background subtraction,” Neural Information Processing 3, 449–458 (2011).
  21. Q. Tu, Y. Xu, and M. Zhou, “Box-based codebook model for real-time objects detection,” in Proceedings of IEEE Conference on Intelligent Control and Automation (IEEE, 2008), pp. 7621–7625.

2014 (1)

2012 (1)

2011 (3)

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

M. Shah, J. Deng, and B. Woodford, “Enhanced codebook model for real-time background subtraction,” Neural Information Processing 3, 449–458 (2011).

O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Trans. Image Process. 20(6), 1709–1724 (2011).
[CrossRef] [PubMed]

2010 (2)

S. Kwak, G. Bae, and H. Byun, “Moving-object segmentation using a foreground history map,” J. Opt. Soc. Am. A 27(2), 180–187 (2010).
[CrossRef] [PubMed]

M. J. Wu and X. R. Peng, “Spatio-temporal context for codebook-based dynamic background subtraction,” AEU, Int. J. Electron. Commun. 64(8), 739–747 (2010).
[CrossRef]

2009 (1)

D. M. Tsai and S. C. Lai, “Independent component analysis-based background subtraction for indoor surveillance,” IEEE Trans. Image Process. 18(1), 158–167 (2009).
[CrossRef] [PubMed]

2008 (1)

L. Maddalena and A. Petrosino, “A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process. 17(7), 1168–1177 (2008).
[CrossRef] [PubMed]

2006 (3)

Z. Zivkovic and F. Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognit. Lett. 27(7), 773–780 (2006).
[CrossRef]

M. Heikklä and M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006).
[CrossRef] [PubMed]

Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
[CrossRef]

2005 (2)

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
[CrossRef]

D. S. Lee, “Effective Gaussian mixture learning for video background subtraction,” IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005).
[CrossRef] [PubMed]

2004 (1)

L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
[CrossRef] [PubMed]

2002 (1)

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
[CrossRef]

Bae, G.

Barnich, O.

O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Trans. Image Process. 20(6), 1709–1724 (2011).
[CrossRef] [PubMed]

Byun, H.

Chalidabhongse, T. H.

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
[CrossRef]

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2004), pp. 3061–3064.

Chen, F.

Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
[CrossRef]

Cuevas, C.

Davis, L.

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
[CrossRef]

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2004), pp. 3061–3064.

Davis, L. S.

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
[CrossRef]

Deng, J.

M. Shah, J. Deng, and B. Woodford, “Enhanced codebook model for real-time background subtraction,” Neural Information Processing 3, 449–458 (2011).

Du, Y. T.

Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
[CrossRef]

Duraiswami, R.

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
[CrossRef]

Elgammal, A.

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
[CrossRef]

Elkabetz, A.

García, N.

Gu, I. Y. H.

L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
[CrossRef] [PubMed]

Guo, J. M.

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

Harwood, D.

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
[CrossRef]

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
[CrossRef]

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2004), pp. 3061–3064.

Heijden, F.

Z. Zivkovic and F. Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognit. Lett. 27(7), 773–780 (2006).
[CrossRef]

Heikklä, M.

M. Heikklä and M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006).
[CrossRef] [PubMed]

Hsia, C. H.

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

Hsu, C. S.

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

Hsu, S. C.

I. T. Sun, S. C. Hsu, and C. L. Huang, “A hybrid codebook background model for background subtraction,” in Workshop of IEEE Conference on Signal Processing Systems (IEEE, 2011), pp. 96–101.
[CrossRef]

Huang, C. L.

I. T. Sun, S. C. Hsu, and C. L. Huang, “A hybrid codebook background model for background subtraction,” in Workshop of IEEE Conference on Signal Processing Systems (IEEE, 2011), pp. 96–101.
[CrossRef]

Huang, W. M.

L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
[CrossRef] [PubMed]

Ilyas, A.

A. Ilyas, M. Scuturici, and S. Miguet, “Real time foreground-background segmentation using a modified codebook model,” in Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (IEEE, 2009), pp. 454–459.
[CrossRef]

Kim, K.

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
[CrossRef]

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2004), pp. 3061–3064.

Kwak, S.

Lai, S. C.

D. M. Tsai and S. C. Lai, “Independent component analysis-based background subtraction for indoor surveillance,” IEEE Trans. Image Process. 18(1), 158–167 (2009).
[CrossRef] [PubMed]

Lee, D. S.

D. S. Lee, “Effective Gaussian mixture learning for video background subtraction,” IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005).
[CrossRef] [PubMed]

Li, L. Y.

L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
[CrossRef] [PubMed]

Li, Y. B.

Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
[CrossRef]

Liu, Y. F.

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

Maddalena, L.

L. Maddalena and A. Petrosino, “A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process. 17(7), 1168–1177 (2008).
[CrossRef] [PubMed]

Miguet, S.

A. Ilyas, M. Scuturici, and S. Miguet, “Real time foreground-background segmentation using a modified codebook model,” in Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (IEEE, 2009), pp. 454–459.
[CrossRef]

Mohedano, R.

Peng, X. R.

M. J. Wu and X. R. Peng, “Spatio-temporal context for codebook-based dynamic background subtraction,” AEU, Int. J. Electron. Commun. 64(8), 739–747 (2010).
[CrossRef]

Petrosino, A.

L. Maddalena and A. Petrosino, “A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process. 17(7), 1168–1177 (2008).
[CrossRef] [PubMed]

Pietikäinen, M.

M. Heikklä and M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006).
[CrossRef] [PubMed]

Scuturici, M.

A. Ilyas, M. Scuturici, and S. Miguet, “Real time foreground-background segmentation using a modified codebook model,” in Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (IEEE, 2009), pp. 454–459.
[CrossRef]

Shah, M.

M. Shah, J. Deng, and B. Woodford, “Enhanced codebook model for real-time background subtraction,” Neural Information Processing 3, 449–458 (2011).

Shih, M. H.

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

Sun, I. T.

I. T. Sun, S. C. Hsu, and C. L. Huang, “A hybrid codebook background model for background subtraction,” in Workshop of IEEE Conference on Signal Processing Systems (IEEE, 2011), pp. 96–101.
[CrossRef]

Tian, Q.

L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
[CrossRef] [PubMed]

Tsai, D. M.

D. M. Tsai and S. C. Lai, “Independent component analysis-based background subtraction for indoor surveillance,” IEEE Trans. Image Process. 18(1), 158–167 (2009).
[CrossRef] [PubMed]

Tu, Q.

Q. Tu, Y. Xu, and M. Zhou, “Box-based codebook model for real-time objects detection,” in Proceedings of IEEE Conference on Intelligent Control and Automation (IEEE, 2008), pp. 7621–7625.

Van Droogenbroeck, M.

O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Trans. Image Process. 20(6), 1709–1724 (2011).
[CrossRef] [PubMed]

Woodford, B.

M. Shah, J. Deng, and B. Woodford, “Enhanced codebook model for real-time background subtraction,” Neural Information Processing 3, 449–458 (2011).

Wu, M. J.

M. J. Wu and X. R. Peng, “Spatio-temporal context for codebook-based dynamic background subtraction,” AEU, Int. J. Electron. Commun. 64(8), 739–747 (2010).
[CrossRef]

Xu, W. L.

Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
[CrossRef]

Xu, Y.

Q. Tu, Y. Xu, and M. Zhou, “Box-based codebook model for real-time objects detection,” in Proceedings of IEEE Conference on Intelligent Control and Automation (IEEE, 2008), pp. 7621–7625.

Yitzhaky, Y.

Zhou, M.

Q. Tu, Y. Xu, and M. Zhou, “Box-based codebook model for real-time objects detection,” in Proceedings of IEEE Conference on Intelligent Control and Automation (IEEE, 2008), pp. 7621–7625.

Zivkovic, Z.

Z. Zivkovic and F. Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognit. Lett. 27(7), 773–780 (2006).
[CrossRef]

Adv. Nat. Comput. (1)

Y. B. Li, F. Chen, W. L. Xu, and Y. T. Du, “Gaussian-based codebook model for video background subtraction,” Adv. Nat. Comput. 2, 762–765 (2006).
[CrossRef]

AEU, Int. J. Electron. Commun. (1)

M. J. Wu and X. R. Peng, “Spatio-temporal context for codebook-based dynamic background subtraction,” AEU, Int. J. Electron. Commun. 64(8), 739–747 (2010).
[CrossRef]

Appl. Opt. (1)

IEEE Trans. Circ. Syst. Video Tech. (1)

J. M. Guo, Y. F. Liu, C. H. Hsia, M. H. Shih, and C. S. Hsu, “Hierarchical method for foreground detection using codebook model,” IEEE Trans. Circ. Syst. Video Tech. 21(6), 804–815 (2011).
[CrossRef]

IEEE Trans. Image Process. (4)

L. Y. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian, “Statistical modeling of complex backgrounds for foreground object detection,” IEEE Trans. Image Process. 13(11), 1459–1472 (2004).
[CrossRef] [PubMed]

L. Maddalena and A. Petrosino, “A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process. 17(7), 1168–1177 (2008).
[CrossRef] [PubMed]

D. M. Tsai and S. C. Lai, “Independent component analysis-based background subtraction for indoor surveillance,” IEEE Trans. Image Process. 18(1), 158–167 (2009).
[CrossRef] [PubMed]

O. Barnich and M. Van Droogenbroeck, “ViBe: a universal background subtraction algorithm for video sequences,” IEEE Trans. Image Process. 20(6), 1709–1724 (2011).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

D. S. Lee, “Effective Gaussian mixture learning for video background subtraction,” IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 827–832 (2005).
[CrossRef] [PubMed]

M. Heikklä and M. Pietikäinen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A (1)

Neural Information Processing (1)

M. Shah, J. Deng, and B. Woodford, “Enhanced codebook model for real-time background subtraction,” Neural Information Processing 3, 449–458 (2011).

Opt. Lett. (1)

Pattern Recognit. Lett. (1)

Z. Zivkovic and F. Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognit. Lett. 27(7), 773–780 (2006).
[CrossRef]

Proc. IEEE (1)

A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 90(7), 1151–1163 (2002).
[CrossRef]

Real-time Imaging (1)

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Real-time foreground-background segmentation using codebook model,” Real-time Imaging 11(3), 172–185 (2005).
[CrossRef]

Other (5)

K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Background modeling and subtraction by codebook construction,” in Proceedings of IEEE Conference on Image Processing (IEEE, 2004), pp. 3061–3064.

M. H. Sigari and M. Fathy, “Real-time background modeling/subtraction using two-layer codebook model,” in Proceedings of the International Multi-Conference of Engineers and Computer Scientists (IAENG, 2008), pp. 717–720.

A. Ilyas, M. Scuturici, and S. Miguet, “Real time foreground-background segmentation using a modified codebook model,” in Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance (IEEE, 2009), pp. 454–459.
[CrossRef]

Q. Tu, Y. Xu, and M. Zhou, “Box-based codebook model for real-time objects detection,” in Proceedings of IEEE Conference on Intelligent Control and Automation (IEEE, 2008), pp. 7621–7625.

I. T. Sun, S. C. Hsu, and C. L. Huang, “A hybrid codebook background model for background subtraction,” in Workshop of IEEE Conference on Signal Processing Systems (IEEE, 2011), pp. 96–101.
[CrossRef]

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Figures (4)

Fig. 1
Fig. 1

Pixel value distribution under complex illumination changes. (The figure in the second row, first column is from Innovative Security Designs (ISD), printed with permission)

Fig. 2
Fig. 2

Arbitrary cylinder color model.

Fig. 3
Fig. 3

AC model updating scheme.

Fig. 4
Fig. 4

Comparative background segmentation for typical frames taken from five sequences. (The figures in the second and third row, first column is from Innovative Security Designs (ISD), printed with permission)

Tables (4)

Tables Icon

Table 1 Parameter settings for the AC and the CY models

Tables Icon

Table 2 Parameter settings for the CO and the BO models

Tables Icon

Table 3 Performance metrics for the AC and the CY color models

Tables Icon

Table 4 Performance metrics for the CO and the BO color models

Equations (15)

Equations on this page are rendered with MathJax. Learn more.

x= BA,PA BA
y= ( PA ) x( BA ) BA
{ y T C T I x BA + T I
a= [ 0 0 ] T
b= [ BA 0 ] T
p= [ x y ] T
y=kx+t
[ 0 b m1 1 1 b m1 1 ( m1 ) b m1 1 x 1 ][ k t ]=[ 0 0 0 y ]
[ k t ]= [ m( 2m1 ) b 2 6( m1 ) + x 2 m b 2 +x m b 2 +x m+1 ] 1 [ xy y ]
[ x y ]= [ k 1 1 k ] 1 [ t x+ky ]
n x = BA BA
n y = ( PA )x n x ( PA )x n x
A * =[ n x n y ] a *
B * =[ n x n y ] b *
{ 2 T I BA 200 T I x l max + T I 0x T C

Metrics