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Article

Background subtraction with dirichlet processes

Authors:

Tom S. F. Haines,

Tao XiangAuthors Info & Claims

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part IV

Pages 99 - 113

https://doi.org/10.1007/978-3-642-33765-9_8

Published: 07 October 2012 Publication History

Abstract

Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a per-pixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adjacent pixels. A new method is presented that uses a Dirichlet process Gaussian mixture model to estimate a per-pixel background distribution, which is followed by probabilistic regularisation. Key advantages include inferring the per-pixel mode count, such that it accurately models dynamic backgrounds, and that it updates its model continuously in a principled way.

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Cited By

Fouzia SBell MKlette R(2017)Deep Learning-Based Improved Object Recognition in WarehousesImage and Video Technology10.1007/978-3-319-75786-5_29(350-365)Online publication date: 20-Nov-2017
https://dl.acm.org/doi/10.1007/978-3-319-75786-5_29
Xin BKawahara YWang YHu LGao W(2016)Efficient Generalized Fused Lasso and Its ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/28474217:4(1-22)Online publication date: 5-May-2016
https://dl.acm.org/doi/10.1145/2847421
Ge WGuo ZDong YChen Y(2016)Dynamic background estimation and complementary learning for pixel-wise foreground/background segmentationPattern Recognition10.1016/j.patcog.2016.01.03159:C(112-125)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.patcog.2016.01.031
Show More Cited By

Background subtraction with dirichlet processes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part IV

October 2012

884 pages

ISBN:9783642337642

Editors:
Andrew Fitzgibbon
Microsoft Research Ltd., Cambridge, UK
,
Svetlana Lazebnik
Dept. of Computer Science, University of North Carolina, Chapel Hill, NC, UK
,
Pietro Perona
Dept. of Computer Science, California Institute of Technology, Pasadena, CA, UK
,
Yoichi Sato
Institute of Industrial Science, The University of Tokyo, Tokyo, CA, Japan
,
Cordelia Schmid
Institute of Industrial Science, INRIA, Montbonnot, CA, France

Sponsors

Adobe
TOYOTA: TOYOTA
Google Inc.
IBMR: IBM Research
Microsoft Reasearch: Microsoft Reasearch

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 October 2012

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Cited By

Fouzia SBell MKlette R(2017)Deep Learning-Based Improved Object Recognition in WarehousesImage and Video Technology10.1007/978-3-319-75786-5_29(350-365)Online publication date: 20-Nov-2017
https://dl.acm.org/doi/10.1007/978-3-319-75786-5_29
Xin BKawahara YWang YHu LGao W(2016)Efficient Generalized Fused Lasso and Its ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/28474217:4(1-22)Online publication date: 5-May-2016
https://dl.acm.org/doi/10.1145/2847421
Ge WGuo ZDong YChen Y(2016)Dynamic background estimation and complementary learning for pixel-wise foreground/background segmentationPattern Recognition10.1016/j.patcog.2016.01.03159:C(112-125)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.patcog.2016.01.031
Fei MLi JLiu H(2015)Visual tracking based on improved foreground detection and perceptual hashingNeurocomputing10.1016/j.neucom.2014.09.060152:C(413-428)Online publication date: 25-Mar-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.09.060
Ghosh SRaptis MSigal LSudderth E(2014)Nonparametric clustering with distance dependent hierarchiesProceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence10.5555/3020751.3020779(260-269)Online publication date: 23-Jul-2014
https://dl.acm.org/doi/10.5555/3020751.3020779
Ling QYan JLi FZhang Y(2014)A background modeling and foreground segmentation approach based on the feedback of moving objects in traffic surveillance systemsNeurocomputing10.1016/j.neucom.2013.11.034133(32-45)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1016/j.neucom.2013.11.034

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