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Cited by 6,923

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Adaptive background mixture models for real-time tracking

Stauffer, C., Grimson, W.E.L.·2003·Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)
DOI 10.1109/cvpr.1999.784637
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Abstract

A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.

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Metadata

Authors
Stauffer, C., & Grimson, W. E. L.
Venue
Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)
Publisher
IEEE Comput. Soc
Published
2003-01-20
Pages
246-252
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.1999.784637
OpenAlex
W2102625004
Cited by
6,923
References
1
FWCI
162.37
Type
conference paper
Language
en
Open access
closed

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