@inproceedings{7ed9a50d91d44a3ebbfc531a1c2deb6e,
title = "One-class background model",
abstract = "Background models are often used in video surveillance systems to find moving objects in an image sequence from a static camera. These models are often built under the assumption that the foreground objects are not known in advance. This assumption has led us to model background using one-class SVM classifiers. Our model belongs to a family of block-based nonparametric models that can be used effectively for highly complex scenes of various background distributions with almost the same configuration parameters for all examined videos. Experimental results are reported on a variety of test videos from the Background Models Challenge (BMC) competition.",
author = "Assaf Glazer and Michael Lindenbaum and Shaul Markovitch",
year = "2013",
month = jan,
day = "1",
doi = "10.1007/978-3-642-37410-4\_26",
language = "English",
isbn = "9783642374098",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 1",
pages = "301--307",
booktitle = "Computer Vision - ACCV 2012 International Workshops, Revised Selected Papers",
address = "Germany",
edition = "PART 1",
note = "11th International Workshop on Asian Conference on Computer Vision, ACCV 2012 ; Conference date: 05-11-2012 Through 06-11-2012",
}