@inproceedings{2008-IUPR-17Mar_1723,
author = {Adrian Ulges and Thomas M. Breuel},
title = {A Local Discriminative Model for Background Subtraction},
booktitle = {DAGM 2008},
year = {2008},
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__utmc = {43439421},
abstract = {Conventional background subtraction techniques that up-
date a background model online have difficulties with correctly segment-
ing foreground objects if sudden brightness changes occur. Other meth-
ods that learn a global scene model offline suffer from projection errors.
To overcome these problems, we present a different approach that is
local and discriminative, i.e. for each pixel a classifier is trained to decide
whether the pixel belongs to the background or foreground. Such a model
requires significantly less tuning effort and shows a better robustness,
as we will demonstrate in quantitative experiments on self-created and
standard benchmarks. Finally, segmentation is improved by 18 % by
integrating the probabilistic evidence provided by the local classifiers
with a graph cut segmentation algorithm.},
category = {image segmentation;classification}
}