TY - GEN
T1 - One-class background model
AU - Glazer, Assaf
AU - Lindenbaum, Michael
AU - Markovitch, Shaul
PY - 2013/4/15
Y1 - 2013/4/15
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84875968638
U2 - 10.1007/978-3-642-37410-4_26
DO - 10.1007/978-3-642-37410-4_26
M3 - Conference contribution
AN - SCOPUS:84875968638
SN - 9783642374098
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 301
EP - 307
BT - Computer Vision - ACCV 2012 International Workshops, Revised Selected Papers
T2 - 11th Asian Conference on Computer Vision, ACCV 2012
Y2 - 5 November 2012 through 6 November 2012
ER -