One-class background model

Assaf Glazer, Michael Lindenbaum, Shaul Markovitch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages301-307
Number of pages7
EditionPART 1
DOIs
StatePublished - 2013
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 5 Nov 20126 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag

Conference

Conference11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of
CityDaejeon
Period5/11/126/11/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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