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.
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© 2013 Springer-Verlag Berlin Heidelberg
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Glazer, A., Lindenbaum, M., Markovitch, S. (2013). One-Class Background Model. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37410-4_26
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DOI: https://doi.org/10.1007/978-3-642-37410-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37409-8
Online ISBN: 978-3-642-37410-4
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