A Local Discriminative Model for Background Subtraction

Adrian Ulges, Thomas Breuel
30th Annual DAGM Symposium, Pages 507-516, Munich, Germany, Springer, 6/2008


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{ ULGE2008,
	Title = {A Local Discriminative Model for Background Subtraction},
	Author = {Adrian Ulges and Thomas Breuel},
	BookTitle = {30th Annual DAGM Symposium},
	Month = {6},
	Year = {2008},
	Publisher = {Springer},
	Pages = {507-516}

Last modified:: 30.08.2016