A Local Discriminative Model for Background Subtraction
Adrian Ulges; Thomas Breuel
In: 30th Annual DAGM Symposium. Annual Symposium of the German Association for Pattern Recognition (DAGM-2008), June 10-13, Munich, Germany, Pages 507-516, Springer, 6/2008.
Zusammenfassung
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.
@inproceedings{pub3755,
author = {
Ulges, Adrian
and
Breuel, Thomas
},
title = {A Local Discriminative Model for Background Subtraction},
booktitle = {30th Annual DAGM Symposium. Annual Symposium of the German Association for Pattern Recognition (DAGM-2008), June 10-13, Munich, Germany},
year = {2008},
month = {6},
pages = {507--516},
publisher = {Springer}
}
Deutsches Forschungszentrum für Künstliche Intelligenz German Research Center for Artificial Intelligence