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Fusing Complementary Operators to Enhance Foreground/Background Segmentation

Fusing Complementary Operators to Enhance Foreground/Background Segmentation
Fusing Complementary Operators to Enhance Foreground/Background Segmentation
Foreground/background segmentation is an active research area for moving object analysis. We combine two probabilistic approaches one of which estimates foreground/background probabilistic density and the other uses prior knowledge to decompose the colour space. The observed performance advantages are associated with the fusion of operators with completely different basis. Tests on outdoor and indoor sequences confirm the efficacy of this approach. The new algorithms can successfully identify and remove shadows and highlights with improved moving-object segmentation. A particular advantage of our evaluation is that it is the first approach that compares foreground/ background labelling with results obtained from labelling by broadcast techniques.
Motion Segmentation, Mixture Models, Foregraound/ Background Labelling, Gait
501-510
Al-Mazeed, Ahmad H.
cf1358d6-9e37-419c-826f-7d4aa025ce7a
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Harvey, Richard
4a98201d-e9d2-4f18-b7d0-b25f4ed474fd
Bagham, J. Andrew
cd70e71d-4d38-4f89-ad7b-9ba6da403a10
Al-Mazeed, Ahmad H.
cf1358d6-9e37-419c-826f-7d4aa025ce7a
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Harvey, Richard
4a98201d-e9d2-4f18-b7d0-b25f4ed474fd
Bagham, J. Andrew
cd70e71d-4d38-4f89-ad7b-9ba6da403a10

Al-Mazeed, Ahmad H., Nixon, Mark S. and Gunn, Steve R. (2003) Fusing Complementary Operators to Enhance Foreground/Background Segmentation. Harvey, Richard and Bagham, J. Andrew (eds.) British Machine Vision Conference 2003. pp. 501-510 .

Record type: Conference or Workshop Item (Paper)

Abstract

Foreground/background segmentation is an active research area for moving object analysis. We combine two probabilistic approaches one of which estimates foreground/background probabilistic density and the other uses prior knowledge to decompose the colour space. The observed performance advantages are associated with the fusion of operators with completely different basis. Tests on outdoor and indoor sequences confirm the efficacy of this approach. The new algorithms can successfully identify and remove shadows and highlights with improved moving-object segmentation. A particular advantage of our evaluation is that it is the first approach that compares foreground/ background labelling with results obtained from labelling by broadcast techniques.

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More information

Published date: 2003
Additional Information: Event Dates: 2003
Venue - Dates: British Machine Vision Conference 2003, 2003-01-01
Keywords: Motion Segmentation, Mixture Models, Foregraound/ Background Labelling, Gait
Organisations: Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 258445
URI: https://eprints.soton.ac.uk/id/eprint/258445
PURE UUID: 45e90b20-17d7-4613-b3a5-9b2672598268
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 20 Nov 2003
Last modified: 06 Jun 2018 13:17

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Contributors

Author: Ahmad H. Al-Mazeed
Author: Mark S. Nixon ORCID iD
Author: Steve R. Gunn
Editor: Richard Harvey
Editor: J. Andrew Bagham

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