Belief propagation in a 3D spatio-temporal MRF for moving object detection

Zhaozheng Yin, Robert Collins

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

60 Scopus citations

Abstract

Previous pixel-level change detection methods either contain a background updating step that is costly for moving cameras (background subtraction) or can not locate object position and shape accurately (frame differencing). In this paper we present a Belief Propagation approach for moving object detection using a 3D Markov Random Field (MRF) model. Each hidden state in the 3D MRF model represents a pixel's motion likelihood and is estimated using message passing in a 6-connected spatio-temporal neighborhood. This approach deals effectively with difficult moving object detection problems like objects camouflaged by similar appearance to the background, or objects with uniform color that frame difference methods can only partially detect. Three examples are presented where moving objects are detected and tracked successfully while handling appearance change, shape change, varied moving speed/direction, scale change and occlusion/clutter.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 12 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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    Yin, Z., & Collins, R. (2007). Belief propagation in a 3D spatio-temporal MRF for moving object detection. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270209] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383184