Abstract

Statistical analysis is widely used for moving object detection from video sequences, and generally it assumes that the recent segmentations are the desirable samples. However, we think the recent segmentations are not desirable for the detection of deformable objects, such as a pedestrian. We present an object detection framework that is designed to select the most desirable samples from all historical segmentations for statistical analysis. Central to this algorithm is that the shape evolution of the deformable object is learned from historical segmentations by an autoregressive model. Based on the learned model, the shape of the moving object in a current frame is predicted. Those historical segmentations that show similar shape to the predicted shape are selected for statistical analysis instead of the recent segmentations. Definitive experiments demonstrate the performance of the proposed method.

© 2009 Optical Society of America

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  1. E. Goujou, J. Miteran, O. Laligant, F. Truchetet, and P. Gorria, “Human detection with a video surveillance system,” IEEE International Conference on Industrial Electronics, Control, and Instrumentation (IEEE, 1995), pp. 1179-1184.
  2. B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008).
    [Crossref] [PubMed]
  3. M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).
  4. B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. Comput. Vis. 77, 259-289 (2008).
    [Crossref]
  5. P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
    [Crossref]
  6. S. Osher and J. A. Sethian, “Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12-49 (1988).
    [Crossref]
  7. D. Cremers, “Dynamical statistical shape priors for level set based tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1262-1273 (2006).
    [Crossref] [PubMed]
  8. I. B. Ayed, S. Li, and I. Ross, “Tracking distributions with an overlap prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).
  9. I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 809-830 (2000).
    [Crossref]
  10. J. Zhong and S. Sclaroff, “Segmenting foreground objects from a dynamic textured background via a robust kalman filter,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 44-50.
    [Crossref]
  11. A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background modeling and subtraction of dynamic scenes,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 1305-1312.
    [Crossref]
  12. M. Harville, G. Gordon, and J. Woodfill, “Foreground segmentation using adaptive mixture models in color and depth,” in IEEE Workshop on Detection and Recognition of Events in Video (IEEE, 2001), pp. 3-11.
    [Crossref]
  13. M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 657-662 (2006).
    [Crossref]
  14. M. Piccardi, “Background subtraction techniques: a review,” IEEE International Conference on Systems, Man and Cybernetics (IEEE, 2004), pp. 3099-3104.
  15. Y. Sheikh and M. Shah, “Bayesian modeling of dynamic scenes for object detection,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1778-1792 (2005).
    [Crossref] [PubMed]
  16. A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 53-60.
  17. C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 780-785 (1997).
    [Crossref]
  18. C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 747-757 (2000).
    [Crossref]
  19. L. Lu and G. D. Hager, “A nonparametric treatment for location/segmentation based visual tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007).
  20. A. Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using non-parametric kernel density estimation for visual surveillance,” Proc. IEEE 90, 1151-1163 (2002).
    [Crossref]
  21. S. Mahamud, “Comparing belief propagation and graph cuts for novelty detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 1154-1159.
  22. A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1499-1504 (2003).
    [Crossref]
  23. C. Yang, R. Duraiswami, N. A. Gumerov, and L. S. Davis, “Improved fast Gauss transform and efficient kernel density estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 664-671.
  24. J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002).
    [Crossref]
  25. K. A. Patwardhan, G. Sapiro, and V. Morellas, “Robust foreground detection in video using pixel layers,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 746-751 (2008).
    [Crossref] [PubMed]
  26. J. Sun, W. Zhang, X. Tang, and H. Y. Shum, “Background cut,” in Proceedings of European Conference on Computer Vision (ECCV, 2006), pp. 628-641.
  27. D. S. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recogn. 37, 1-19 (2004).
    [Crossref]
  28. D. S. Zhang and G. Lu, “Generic Fourier descriptor for shape-based image retrieval,” in Proceedings of IEEE International Conference on Multimedia and Expo (ICME, 2000), pp. 425-428.
  29. G. Schwarz, “Estimating the dimension of a model,” Ann. Stat. 6, 461-464 (1978).
    [Crossref]
  30. S. Haykin, Adaptive Filter Theory (Prentice Hall, 2001).
  31. V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 147-159 (2004).
    [Crossref] [PubMed]
  32. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239 (2001).
    [Crossref]
  33. D. Greig, B. Porteous, and A. Seheult, “Exact maximum a posteriori estimation for binary images,” J. R. Stat. Soc. Ser. B (Methodol.) 51, 271-279 (1989).

2008 (3)

B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008).
[Crossref] [PubMed]

B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. Comput. Vis. 77, 259-289 (2008).
[Crossref]

K. A. Patwardhan, G. Sapiro, and V. Morellas, “Robust foreground detection in video using pixel layers,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 746-751 (2008).
[Crossref] [PubMed]

2006 (2)

D. Cremers, “Dynamical statistical shape priors for level set based tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1262-1273 (2006).
[Crossref] [PubMed]

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 657-662 (2006).
[Crossref]

2005 (2)

Y. Sheikh and M. Shah, “Bayesian modeling of dynamic scenes for object detection,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1778-1792 (2005).
[Crossref] [PubMed]

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[Crossref]

2004 (2)

D. S. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recogn. 37, 1-19 (2004).
[Crossref]

V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 147-159 (2004).
[Crossref] [PubMed]

2003 (1)

A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1499-1504 (2003).
[Crossref]

2002 (2)

J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002).
[Crossref]

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

2001 (1)

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239 (2001).
[Crossref]

2000 (2)

C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 747-757 (2000).
[Crossref]

I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 809-830 (2000).
[Crossref]

1997 (1)

C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 780-785 (1997).
[Crossref]

1989 (1)

D. Greig, B. Porteous, and A. Seheult, “Exact maximum a posteriori estimation for binary images,” J. R. Stat. Soc. Ser. B (Methodol.) 51, 271-279 (1989).

1988 (1)

S. Osher and J. A. Sethian, “Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12-49 (1988).
[Crossref]

1978 (1)

G. Schwarz, “Estimating the dimension of a model,” Ann. Stat. 6, 461-464 (1978).
[Crossref]

Andriluka, M.

M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).

Ayed, I. B.

I. B. Ayed, S. Li, and I. Ross, “Tracking distributions with an overlap prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).

Azarbayejani, A.

C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 780-785 (1997).
[Crossref]

Blake, A.

J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002).
[Crossref]

A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 53-60.

Boykov, Y.

Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239 (2001).
[Crossref]

Cornelis, N.

B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008).
[Crossref] [PubMed]

Cremers, D.

D. Cremers, “Dynamical statistical shape priors for level set based tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 1262-1273 (2006).
[Crossref] [PubMed]

Criminisi, A.

A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 53-60.

Cross, G.

A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 53-60.

Darrell, T.

C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 780-785 (1997).
[Crossref]

Davis, L. S.

A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1499-1504 (2003).
[Crossref]

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

I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 809-830 (2000).
[Crossref]

C. Yang, R. Duraiswami, N. A. Gumerov, and L. S. Davis, “Improved fast Gauss transform and efficient kernel density estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 664-671.

Duraiswami, R.

A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1499-1504 (2003).
[Crossref]

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

C. Yang, R. Duraiswami, N. A. Gumerov, and L. S. Davis, “Improved fast Gauss transform and efficient kernel density estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 664-671.

Elgammal, A.

A. Elgammal, R. Duraiswami, and L. S. Davis, “Efficient kernel density estimation using the fast Gauss transform with applications to color modeling and tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1499-1504 (2003).
[Crossref]

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

Felzenszwalb, P. F.

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[Crossref]

Gool, L. V.

B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008).
[Crossref] [PubMed]

Gordon, G.

M. Harville, G. Gordon, and J. Woodfill, “Foreground segmentation using adaptive mixture models in color and depth,” in IEEE Workshop on Detection and Recognition of Events in Video (IEEE, 2001), pp. 3-11.
[Crossref]

Gorria, P.

E. Goujou, J. Miteran, O. Laligant, F. Truchetet, and P. Gorria, “Human detection with a video surveillance system,” IEEE International Conference on Industrial Electronics, Control, and Instrumentation (IEEE, 1995), pp. 1179-1184.

Goujou, E.

E. Goujou, J. Miteran, O. Laligant, F. Truchetet, and P. Gorria, “Human detection with a video surveillance system,” IEEE International Conference on Industrial Electronics, Control, and Instrumentation (IEEE, 1995), pp. 1179-1184.

Greig, D.

D. Greig, B. Porteous, and A. Seheult, “Exact maximum a posteriori estimation for binary images,” J. R. Stat. Soc. Ser. B (Methodol.) 51, 271-279 (1989).

Grimson, W. E. L.

C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 747-757 (2000).
[Crossref]

Gumerov, N. A.

C. Yang, R. Duraiswami, N. A. Gumerov, and L. S. Davis, “Improved fast Gauss transform and efficient kernel density estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 664-671.

Hager, G. D.

L. Lu and G. D. Hager, “A nonparametric treatment for location/segmentation based visual tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007).

Haritaoglu, I.

I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 809-830 (2000).
[Crossref]

Harville, M.

M. Harville, G. Gordon, and J. Woodfill, “Foreground segmentation using adaptive mixture models in color and depth,” in IEEE Workshop on Detection and Recognition of Events in Video (IEEE, 2001), pp. 3-11.
[Crossref]

Harwood, D.

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

I. Haritaoglu, D. Harwood, and L. S. Davis, “W4: Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 809-830 (2000).
[Crossref]

Haykin, S.

S. Haykin, Adaptive Filter Theory (Prentice Hall, 2001).

Heikkila, M.

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 657-662 (2006).
[Crossref]

Huttenlocher, D. P.

P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Int. J. Comput. Vis. 61, 55-79 (2005).
[Crossref]

Joga, S.

J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002).
[Crossref]

Kato, J.

J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002).
[Crossref]

Kolmogorov, V.

V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 26, 147-159 (2004).
[Crossref] [PubMed]

A. Criminisi, G. Cross, A. Blake, and V. Kolmogorov, “Bilayer segmentation of live video,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 53-60.

Laligant, O.

E. Goujou, J. Miteran, O. Laligant, F. Truchetet, and P. Gorria, “Human detection with a video surveillance system,” IEEE International Conference on Industrial Electronics, Control, and Instrumentation (IEEE, 1995), pp. 1179-1184.

Leibe, B.

B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. Comput. Vis. 77, 259-289 (2008).
[Crossref]

B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008).
[Crossref] [PubMed]

Leonardis, A.

B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. Comput. Vis. 77, 259-289 (2008).
[Crossref]

Li, S.

I. B. Ayed, S. Li, and I. Ross, “Tracking distributions with an overlap prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).

Lu, G.

D. S. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recogn. 37, 1-19 (2004).
[Crossref]

D. S. Zhang and G. Lu, “Generic Fourier descriptor for shape-based image retrieval,” in Proceedings of IEEE International Conference on Multimedia and Expo (ICME, 2000), pp. 425-428.

Lu, L.

L. Lu and G. D. Hager, “A nonparametric treatment for location/segmentation based visual tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007).

Mahamud, S.

S. Mahamud, “Comparing belief propagation and graph cuts for novelty detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 1154-1159.

Miteran, J.

E. Goujou, J. Miteran, O. Laligant, F. Truchetet, and P. Gorria, “Human detection with a video surveillance system,” IEEE International Conference on Industrial Electronics, Control, and Instrumentation (IEEE, 1995), pp. 1179-1184.

Mittal, A.

A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background modeling and subtraction of dynamic scenes,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 1305-1312.
[Crossref]

Monnet, A.

A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background modeling and subtraction of dynamic scenes,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 1305-1312.
[Crossref]

Morellas, V.

K. A. Patwardhan, G. Sapiro, and V. Morellas, “Robust foreground detection in video using pixel layers,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 746-751 (2008).
[Crossref] [PubMed]

Osher, S.

S. Osher and J. A. Sethian, “Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12-49 (1988).
[Crossref]

Paragios, N.

A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background modeling and subtraction of dynamic scenes,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 1305-1312.
[Crossref]

Patwardhan, K. A.

K. A. Patwardhan, G. Sapiro, and V. Morellas, “Robust foreground detection in video using pixel layers,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 746-751 (2008).
[Crossref] [PubMed]

Pentland, A. P.

C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 780-785 (1997).
[Crossref]

Piccardi, M.

M. Piccardi, “Background subtraction techniques: a review,” IEEE International Conference on Systems, Man and Cybernetics (IEEE, 2004), pp. 3099-3104.

Pietikainen, M.

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 657-662 (2006).
[Crossref]

Porteous, B.

D. Greig, B. Porteous, and A. Seheult, “Exact maximum a posteriori estimation for binary images,” J. R. Stat. Soc. Ser. B (Methodol.) 51, 271-279 (1989).

Ramesh, V.

A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background modeling and subtraction of dynamic scenes,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 1305-1312.
[Crossref]

Rittscher, J.

J. Kato, T. Watanabe, S. Joga, J. Rittscher, and A. Blake, “An HMM-based segmentation method for traffic monitoring movies,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 1291-1296 (2002).
[Crossref]

Ross, I.

I. B. Ayed, S. Li, and I. Ross, “Tracking distributions with an overlap prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).

Roth, S.

M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).

Sapiro, G.

K. A. Patwardhan, G. Sapiro, and V. Morellas, “Robust foreground detection in video using pixel layers,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 746-751 (2008).
[Crossref] [PubMed]

Schiele, B.

B. Leibe, A. Leonardis, and B. Schiele, “Robust object detection with interleaved categorization and segmentation,” Int. J. Comput. Vis. 77, 259-289 (2008).
[Crossref]

M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008).

Schindler, K.

B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 1683-1698 (2008).
[Crossref] [PubMed]

Schwarz, G.

G. Schwarz, “Estimating the dimension of a model,” Ann. Stat. 6, 461-464 (1978).
[Crossref]

Sclaroff, S.

J. Zhong and S. Sclaroff, “Segmenting foreground objects from a dynamic textured background via a robust kalman filter,” IEEE International Conference on Computer Vision (IEEE, 2003), pp. 44-50.
[Crossref]

Seheult, A.

D. Greig, B. Porteous, and A. Seheult, “Exact maximum a posteriori estimation for binary images,” J. R. Stat. Soc. Ser. B (Methodol.) 51, 271-279 (1989).

Sethian, J. A.

S. Osher and J. A. Sethian, “Fronts propagation with curvature dependent speed: algorithms based on Hamilton-Jacobi formulations,” J. Comput. Phys. 79, 12-49 (1988).
[Crossref]

Shah, M.

Y. Sheikh and M. Shah, “Bayesian modeling of dynamic scenes for object detection,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1778-1792 (2005).
[Crossref] [PubMed]

Sheikh, Y.

Y. Sheikh and M. Shah, “Bayesian modeling of dynamic scenes for object detection,” IEEE Trans. Pattern Anal. Mach. Intell. 27, 1778-1792 (2005).
[Crossref] [PubMed]

Shum, H. Y.

J. Sun, W. Zhang, X. Tang, and H. Y. Shum, “Background cut,” in Proceedings of European Conference on Computer Vision (ECCV, 2006), pp. 628-641.

Stauffer, C.

C. Stauffer and W. E. L. Grimson, “Learning patterns of activity using real-time tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 22, 747-757 (2000).
[Crossref]

Sun, J.

J. Sun, W. Zhang, X. Tang, and H. Y. Shum, “Background cut,” in Proceedings of European Conference on Computer Vision (ECCV, 2006), pp. 628-641.

Tang, X.

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C. Yang, R. Duraiswami, N. A. Gumerov, and L. S. Davis, “Improved fast Gauss transform and efficient kernel density estimation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), pp. 664-671.

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Figures (8)

Fig. 1
Fig. 1

(a) Frame from an image sequence. (b) Binary segmentation of (a) by Sheikh’s algorithm. (c) Reconstructed image of (b) from its Fourier descriptor. (d) Reconstructed image of (b) from the predicted shape vector.

Fig. 2
Fig. 2

Elements in φ f and ψ f of Fig. 1a are shown in (a) and (b), respectively.

Fig. 3
Fig. 3

(a) Three frames of the first test sequence. (b) Segmentations by Sheikh’s algorithm. (c) Segmentations by the proposed algorithm.

Fig. 4
Fig. 4

Elements in φ f and ψ f of the bottom image in Fig. 3a are shown in (a) and (b), respectively.

Fig. 5
Fig. 5

First row shows three typical images of the second test sequence. Segmentations of four frames by Sheikh’s algorithm and the proposed algorithm are shown in the middle and bottom rows, respectively.

Fig. 6
Fig. 6

Elements in φ f and ψ f of the bottom-left image in Fig. 5 are shown in (a) and (b), respectively.

Fig. 7
Fig. 7

(a) Two frames of the third test sequence. Segmentations by Sheikh’s algorithm and the proposed algorithm are shown in (b) and (c), respectively.

Fig. 8
Fig. 8

(a) Precision. (b) Recall.

Equations (11)

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G t = J 1 G t 1 + J 2 G t 2 + + J k G t k + ε ,
p ( z n φ b n ) = 1 κ b * M 2 q = 1 Q E H ( z n u q ) ,
d ( G ̑ t , G t i ) = 1 ( r = 1 U 4 G ̑ r t G r t i ) .
p ( z n ψ f n , φ f n ) = 1 2 κ f * M 2 [ q = 1 Q 1 E H ( z n u q ) + q = 1 Q 2 E H ( z n u q ) ] ,
η ( z n ) = ln [ p ( z n ψ f n , φ f n ) ] ln [ p ( z n φ b n ) ]
l n = { background , if η ( z n ) > τ foreground , otherwise } ,
p ( L ) exp { i = 1 N j = 1 N ω [ l i l j + ( 1 l i ) ( 1 l j ) ] } ,
p ( L Z ) = n = 1 N l n * η ( z n ) + ln [ p ( L ) ] .
H = [ 25 0 0 0 25 0 0 0 25 ] .
Precision = # of true positives detected total # of positives detected ,
Recall = # of true positives detected total # of true positives in ground truth .

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