Understanding Background Mixture Models for Foreground Segmentation 论文

摘要

The seminal video surveillance papers on moving object segmentation through adaptive Gaussian mixture models of the background image do not provide adequate information for easy replication of the work. They also do not explicitly base their algorithms on the underlying statistical theory and sometimes even suffer from errors of derivation. This tutorial paper describes a practical implementation of the Stauffer-Grimson algorithm and provides values for all model parameters. It also shows what approximations to the theory were made and how to improve the standard algorithm by redefining those approximations.