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Real-Time Adaptive Foreground/Background Segmentation

Butler, Darren E., Bove, V. Michael, & Sridharan, Sridha (2005) Real-Time Adaptive Foreground/Background Segmentation. EURASIP Journal on Applied Signal Processing, 2005, pp. 2292-2304.

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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 320×240 PAL video at full frame rate using only 35%–40% of a 1.8 GHz Pentium 4 computer.

Impact and interest:

18 citations in Scopus
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5 citations in Web of Science®

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Full-text downloads:

600 since deposited on 15 Jan 2008
19 in the past twelve months

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ID Code: 11949
Item Type: Journal Article
Additional Information: The contents of this journal can be freely accessed online via the journal’s web page (see hypertext link).
Keywords: video segmentation, background segmentation, real, time video processing
DOI: 10.1155/ASP.2005.2292
ISSN: 1110-8657
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2005 Hindawi Publishing Corporation
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 15 Jan 2008
Last Modified: 29 Feb 2012 23:17

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