Adaptive Background Mixture Models for Real-Time Tracking

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.

DOI: 10.1109/CVPR.1999.784637
View Slides

Extracted Key Phrases

4 Figures and Tables

0200400'99'01'03'05'07'09'11'13'15'17
Citations per Year

5,543 Citations

Semantic Scholar estimates that this publication has 5,543 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Stauffer1999AdaptiveBM, title={Adaptive Background Mixture Models for Real-Time Tracking}, author={Chris Stauffer and W. Eric L. Grimson}, booktitle={CVPR}, year={1999} }