Real-time segmentation of objects from video sequences with non-stationary backgrounds using spatio-temporal coherence

Jae Kyun Ahn, Chang-Su Kim

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

10 Citations (Scopus)

Abstract

A real-time video segmentation algorithm, which can extract objects from video sequences even with non-stationary backgrounds, is proposed in this work. First, we segment the first frame into an object and a background interactively to build the probability density functions of colors in the object and the background. Then, for each subsequent frame, we construct a coherence strip, which is likely to contain the object contour, by exploiting spatio-temporal correlations. Finally, we perform the segmentation by minimizing an energy function composed of color, coherence, and smoothness terms. Experimental results on various test sequences show that the proposed algorithm provides accurate segmentation results in real-time, even though video sequences contain unstable camera motions.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Pages1544-1547
Number of pages4
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, United States
Duration: 2008 Oct 122008 Oct 15

Publication series

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

Other

Other2008 IEEE International Conference on Image Processing, ICIP 2008
Country/TerritoryUnited States
CitySan Diego, CA
Period08/10/1208/10/15

Keywords

  • Graph cut
  • Kernel density estimation
  • Segmentation
  • Spatio-temporal coherence
  • Video object

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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