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Improved background mixture models for video surveillance applications

Chris Poppe (UGent) , Gaëtan Martens (UGent) , Peter Lambert (UGent) and Rik Van de Walle (UGent)
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Abstract
Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed. This paper proposes an update of the popular Mixture of Gaussian Models technique. Experimental analysis shows a lack of this technique to cope with quick illumination changes. A different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. Finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.
Keywords
video surveillance, object detection

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Chicago
Poppe, Chris, Gaëtan Martens, Peter Lambert, and Rik Van de Walle. 2007. “Improved Background Mixture Models for Video Surveillance Applications.” In Lecture Notes in Computer Science, ed. Y Yagi, SB Kang, IS Kweon, and H Zha, 4843:251–260. Berlin, Germany: Springer.
APA
Poppe, Chris, Martens, G., Lambert, P., & Van de Walle, R. (2007). Improved background mixture models for video surveillance applications. In Y. Yagi, S. Kang, I. Kweon, & H. Zha (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 4843, pp. 251–260). Presented at the 8th Asian Conference on Computer Vision (ACCV 2007), Berlin, Germany: Springer.
Vancouver
1.
Poppe C, Martens G, Lambert P, Van de Walle R. Improved background mixture models for video surveillance applications. In: Yagi Y, Kang S, Kweon I, Zha H, editors. LECTURE NOTES IN COMPUTER SCIENCE. Berlin, Germany: Springer; 2007. p. 251–60.
MLA
Poppe, Chris, Gaëtan Martens, Peter Lambert, et al. “Improved Background Mixture Models for Video Surveillance Applications.” Lecture Notes in Computer Science. Ed. Y Yagi et al. Vol. 4843. Berlin, Germany: Springer, 2007. 251–260. Print.
@inproceedings{416935,
  abstract     = {Background subtraction is a method commonly used to segment objects of interest in image sequences. By comparing new frames to a background model, regions of interest can be found. To cope with highly dynamic and complex environments, a mixture of several models has been proposed. This paper proposes an update of the popular Mixture of Gaussian Models technique. Experimental analysis shows a lack of this technique to cope with quick illumination changes. A different matching mechanism is proposed to improve the general robustness and a comparison with related work is given. Finally, experimental results are presented to show the gain of the updated technique, according to the standard scheme and the related techniques.},
  author       = {Poppe, Chris and Martens, Ga{\"e}tan and Lambert, Peter and Van de Walle, Rik},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  editor       = {Yagi, Y and Kang, SB and Kweon, IS and Zha, H},
  isbn         = {9783540763857},
  issn         = {0302-9743},
  language     = {eng},
  location     = {Tokyo, Japan},
  pages        = {251--260},
  publisher    = {Springer},
  title        = {Improved background mixture models for video surveillance applications},
  url          = {http://dx.doi.org/10.1007/978-3-540-76386-4\_23},
  volume       = {4843},
  year         = {2007},
}

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