Paper Info
Title
Efficient adaptive background subtraction based on multi-resolution background modelling and updating
Abstract
Adaptive background subtraction (ABS) is a fundamental step for foreground object detection in many real-time video surveillance systems. In many ABS methods, a pixel-based statistical model is used for the background and each pixel is updated online to adapt to various background changes. As a result, heavy computation and memory consumption are required. In this paper, we propose an efficient methodology for implementation of ABS algorithms based on multiresolution background modelling and sequential sampling for updating background. Experiments and quantitative evaluation are conducted on two open data sets (PETS2001 and PETS2006) and scenarios captured in some public places, and some results are included. Our results have shown that the proposed method requires a significant reduction in memory and CPU usage, meanwhile maintaining a similar foreground segmentation performance as compared with the corresponding single resolution methods.
Year
DOI
Venue
2007
10.1007/978-3-540-77255-2_14
PCM
Keywords
Field
DocType
efficient methodology,corresponding single resolution method,efficient adaptive background subtraction,memory consumption,cpu usage,multi-resolution background modelling,similar foreground segmentation performance,adaptive background subtraction,abs method,foreground object detection,multiresolution background modelling,various background change,statistical modelling,statistical model,sequential sampling,real time,background subtraction
Sequential sampling,Background subtraction,Object detection,Computer vision,Pattern recognition,CPU time,Segmentation,Computer science,Statistical model,Artificial intelligence,Pixel,Computation
Conference
Volume
ISSN
ISBN
4810
0302-9743
3-540-77254-5
Citations 
PageRank 
References 
1
0.37
9
Authors
3
Name
Order
Citations
PageRank
Ruijiang Luo1313.08
Liyuan Li291261.31
Irene Yu-Hua Gu361335.06