Paper Info
Title
Hybrid center-symmetric local pattern for dynamic background subtraction
Abstract
Effective foreground detection in dynamic scenes is a challenging task in computer vision applications. In this paper, we propose a novel background modeling method to tackle this problem. First, we propose a second-order center-symmetric local derivative pattern (CS-LDP) which extracts more detail information compared with the first-order center-symmetric local binary pattern (CS-LBP). Then by concatenating the CS-LBP and CS-LDP histograms, a new hybrid histogram feature is presented. The length of this histogram is much shorter than the local binary pattern (LBP) histogram. Based on this hybrid feature, a novel background modeling method is proposed where the pixel process is modeled with a group of adaptive hybrid histograms. The major advantage of our method is its low complexity. Experiments on three challenging sequences demonstrate that the proposed method is effective and fast, producing comparable results to state-of-art algorithm while reducing the computation time greatly.
Year
DOI
Venue
2011
10.1109/ICME.2011.6011859
ICME
Keywords
Field
DocType
new hybrid histogram feature,adaptive hybrid histogram,local binary pattern,center-symmetric local derivative pattern,dynamic background subtraction,hybrid feature,first-order center-symmetric local binary,hybrid center-symmetric local pattern,novel background modeling method,challenging sequence,cs-ldp histogram,histograms,estimation,second order,computer vision,kernel,computational modeling,data mining,background subtraction,first order,feature extraction
Kernel (linear algebra),Background subtraction,Computer vision,Histogram,Pattern recognition,Computer science,Local binary patterns,Histogram matching,Foreground detection,Feature extraction,Artificial intelligence,Pixel
Conference
ISSN
Citations 
PageRank 
1945-7871
13
0.61
References 
Authors
11
4
Name
Order
Citations
PageRank
Gengjian Xue1825.89
Li Song232365.87
Jun Sun37611.28
Meng Wu4141.29