Paper Info
Title
Dynamic background subtraction based on spatial extended center-symmetric local binary pattern
Abstract
Moving objects detection in dynamic scenes is a challenging task in many computer vision applications. Traditional background modeling methods do not work well in these situations since they assume a nearly static background. In this paper, a novel operator named spatial extended center-symmetric local binary pattern (SCS-LBP) for background modeling is proposed. It extracts spatial and temporal information simultaneously while has low complexity compared to the local binary pattern (LBP) operator. Then combining this operator with an improved temporal distribution estimation scheme, we propose a new background subtraction method. In our method, each pixel is modeled by a group of adaptive SCS-LBP histograms, which provides us with many advantages compared to traditional ones. Experimental results demonstrate the effectiveness and robustness of our method.
Year
DOI
Venue
2010
10.1109/ICME.2010.5582601
ICME
Keywords
Field
DocType
background subtraction method,local binary pattern,spatial extended center-symmetric pattern,image sequences,object detection,computer vision,spatial extended center-symmetric local binary pattern,moving object detection,background modeling,online estimation,background modeling methods,temporal distribution estimation scheme,image motion analysis,estimation,pixel,computational modeling,background subtraction,robustness,data mining,histograms
Background subtraction,Object detection,Computer vision,Histogram,Pattern recognition,Computer science,Local binary patterns,Robustness (computer science),Operator (computer programming),Artificial intelligence,Pixel
Conference
ISSN
ISBN
Citations 
1945-7871
978-1-4244-7491-2
7
PageRank 
References 
Authors
0.44
10
3
Name
Order
Citations
PageRank
Gengjian Xue1825.89
Jun Sun27611.28
Li Song332365.87