Paper Info
Title
An eigenbackground subtraction method using recursive error compensation
Abstract
Eigenbackground subtraction is a commonly used method for moving object detection. The method uses the difference between an input image and the reconstructed background image for detecting foreground objects based on eigenvalue decomposition. In the method, foreground regions are represented in the reconstructed image using eigenbackground in the sense of least mean squared error minimisation. This results in errors that are spread over the entire reconstructed reference image. This will also result in degradation of quality of reconstructed background leading to inaccurate moving object detection. In order to compensate these regions, an efficient method is proposed by using recursive error compensation and an adaptively computed threshold. Experiments were conducted on a range of image sequences with variety of complexity. Performance were evaluated both qualitatively and quantitatively. Comparisons made with two existing methods have shown better approximations of the background images and more accurate detection of foreground objects have been achieved by the proposed method.
Year
DOI
Venue
2006
10.1007/11922162_89
PCM
Keywords
Field
DocType
eigenbackground subtraction method,image sequence,existing method,background image,object detection,input image,efficient method,foreground object,reconstructed background image,entire reconstructed reference image,recursive error compensation,object tracking,principal component analysis,background subtraction,least mean square,eigenvalue decomposition
Background subtraction,Iterative reconstruction,Computer vision,Object detection,Image sensor,Pattern recognition,Computer science,Mean squared error,Image processing,Video tracking,Artificial intelligence,Subtraction
Conference
Volume
ISSN
ISBN
4261
0302-9743
3-540-48766-2
Citations 
PageRank 
References 
7
0.51
13
Authors
3
Name
Order
Citations
PageRank
Zhifei Xu191.24
Pengfei Shi289167.98
Irene Yu-Hua Gu361335.06