Background Estimation as a Labeling Problem

Authors:
Scott Cohen
Affiliations:
Adobe Systems Inc.
Venue:
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Year:
2005

Citing 0
Cited 7

Practical background estimation for mosaic blending with patch-based Markov random fields

Pattern Recognition
Background estimation from non-time sequence images

GI '08 Proceedings of graphics interface 2008
Learning a scene background model via classification

IEEE Transactions on Signal Processing
Background estimation using graph cuts and inpainting

Proceedings of Graphics Interface 2010
A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts

Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Synthetic aperture imaging using pixel labeling via energy minimization

Pattern Recognition
Background subtraction with dirichlet processes

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

Quantified Score

Hi-index	0.01

Visualization

Abstract

We present a new background estimation algorithm that constructs the background of an image sequence with moving objects by copying areas from input frames. The background estimation problem is formulated as an optimal labeling problem in which the label at an output pixel is the frame number from which to copy the background color. The costs of assigning labels encourage seamless copying from regions that are stationary over a period of time in such a way that implied motion boundaries occur at intensity edges. This is accomplished without explicitly tracking the moving objects or computing optical flow. Experiments demonstrate that our algorithm is effective in difficult areas where the background is visible for only a small fraction of time, and on inputs with both moving objects that are not always in motion and moving objects with textureless areas.