Base selection in estimating sparse foreground in video

Mert Dikmen, Shen Fu Tsai, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We investigate effective means of building robust dictionaries for detecting the sparse foreground in videos with static background. This work is an extension to our existing solution [1] to foreground/background segmentation problem using the linear programming method [2] proposed to detect sparse errors in signals, which are created by a known dictionary. The dictionary building methods we study are established robust component analysis techniques in the literature (i.e. k-SVD [3] & robust-PCA [4]) as well as a heuristic (running median) inspired by the highly correlated nature of the static video background signal. We compare the effectiveness of the new methods with our original system as well as a baseline method, which is the commonly used single Gaussian model of the background pixels.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages3217-3220
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

Keywords

  • Background subtraction

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Dikmen, M., Tsai, S. F., & Huang, T. S. (2009). Base selection in estimating sparse foreground in video. In 2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings (pp. 3217-3220). [5414368] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2009.5414368