DocumentCode
625113
Title
Eigenbackground Bootstrapping
Author
Hughes, Kit ; Grzeda, Victor ; Greenspan, Marshall
Author_Institution
Electr. & Comput. Eng., Queen´s Univ., Kingston, ON, Canada
fYear
2013
fDate
28-31 May 2013
Firstpage
196
Lastpage
201
Abstract
-A new method for initializing Eigenbackground is proposed. The approach does not require supervised or lengthy training, but instead is bootstrapped as a single unobstructed background frame is used to exploit spatial information in place of gathering a temporal history to generate pixel statistics. Experimental results indicate that the bootstrapped Eigenbackground performed comparable to and sometimes better than the supervised Eigenbackground on a standard background subtraction data set.
Keywords
eigenvalues and eigenfunctions; image segmentation; statistical analysis; video signal processing; Eigenbackground bootstrapping; pixel statistics; spatial information; standard background subtraction data set; supervised Eigenbackground; unobstructed background frame; Adaptation models; Heuristic algorithms; Principal component analysis; Road transportation; Training; Training data; Vectors; Background Subtraction; Principal Component Analysis; Subspace Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Robot Vision (CRV), 2013 International Conference on
Conference_Location
Regina, SK
Print_ISBN
978-1-4673-6409-6
Type
conf
DOI
10.1109/CRV.2013.47
Filename
6569203
Link To Document