Real-Time Adaptive Foreground/Background Segmentation
Name
1687-6180-2005-841926.pdf
Size
2.29 MB
Format
Adobe PDF
Checksum (MD5)
371b7a093541544410fc7c306f03301d
Author(s)
Butler, Darren E.
Bove, V. Michael, Jr.
Date Issued
August 2005
Journal
EURASIP Journal on Advances in Signal Processing
Publisher
Springer
Citation
EURASIP Journal on Advances in Signal Processing. 2005 Aug 25;2005(14):841926
Version
Author's final manuscript
Abstract
The automatic analysis of digital video scenes often requires the segmentation of moving objects from a static background. Historically, algorithms developed for this purpose have been restricted to small frame sizes, low frame rates, or offline processing. The simplest approach involves subtracting the current frame from the known background. However, as the background is rarely known beforehand, the key is how to learn and model it. This paper proposes a new algorithm that represents each pixel in the frame by a group of clusters. The clusters are sorted in order of the likelihood that they model the background and are adapted to deal with background and lighting variations. Incoming pixels are matched against the corresponding cluster group and are classified according to whether the matching cluster is considered part of the background. The algorithm has been qualitatively and quantitatively evaluated against three other well-known techniques. It demonstrated equal or better segmentation and proved capable of processing PAL video at full frame rate using only 35%–40% of a GHz Pentium 4 computer.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1155/ASP.2005.2292
