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Classifiers Combination for Improved Motion Segmentation

Classifiers Combination for Improved Motion Segmentation
Classifiers Combination for Improved Motion Segmentation
Abstract. Multiple classifiers have shown capability to improve performance in pattern recognition. This process can improve the overall accuracy of the system by using an optimal decision criteria. In this paper we propose an approach using a weighted benevolent fusion strategy to combine two state of the art pixel based motion classifiers. Tests on outdoor and indoor sequences confirm the efficacy of this approach. The new algorithm can successfully identify and remove shadows and highlights with improved moving-object segmentation. A process to optimise shadow removal is introduced to remove shadows and distinguish them from motion pixels. A particular advantage of our evaluation is that it is the first approach that compares foreground/background labelling with results obtained from ground truth labelling.
Motion estimation, Background Subtraction, Fusing Classifiers
3-540-23240-0
363-371
Al-Mazeed, A. H.
225498a7-95f2-41a8-b53b-0d835ff8a908
Nixon, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Gunn, S. R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Campilho, A.
746dfd65-41c3-44bf-847c-e22570ae7814
Kamel, M.
75ff77d1-26ce-4f0f-99f7-5014c79128c7
Al-Mazeed, A. H.
225498a7-95f2-41a8-b53b-0d835ff8a908
Nixon, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Gunn, S. R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Campilho, A.
746dfd65-41c3-44bf-847c-e22570ae7814
Kamel, M.
75ff77d1-26ce-4f0f-99f7-5014c79128c7

Al-Mazeed, A. H., Nixon, M. S. and Gunn, S. R. (2004) Classifiers Combination for Improved Motion Segmentation. Campilho, A. and Kamel, M. (eds.) International Conference on Image Analysis and Recognition, Portugal. 29 Sep - 01 Oct 2004. pp. 363-371 .

Record type: Conference or Workshop Item (Other)

Abstract

Abstract. Multiple classifiers have shown capability to improve performance in pattern recognition. This process can improve the overall accuracy of the system by using an optimal decision criteria. In this paper we propose an approach using a weighted benevolent fusion strategy to combine two state of the art pixel based motion classifiers. Tests on outdoor and indoor sequences confirm the efficacy of this approach. The new algorithm can successfully identify and remove shadows and highlights with improved moving-object segmentation. A process to optimise shadow removal is introduced to remove shadows and distinguish them from motion pixels. A particular advantage of our evaluation is that it is the first approach that compares foreground/background labelling with results obtained from ground truth labelling.

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More information

Published date: 2004
Additional Information: Lecture Notes in Computer Science Event Dates: September 29 - October 1, 2004
Venue - Dates: International Conference on Image Analysis and Recognition, Portugal, 2004-09-29 - 2004-10-01
Keywords: Motion estimation, Background Subtraction, Fusing Classifiers
Organisations: Electronic & Software Systems, Southampton Wireless Group

Identifiers

Local EPrints ID: 260073
URI: https://eprints.soton.ac.uk/id/eprint/260073
ISBN: 3-540-23240-0
PURE UUID: 737aaf33-0015-4d8e-a64e-667ca60dbe2a
ORCID for M. S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 30 Oct 2004
Last modified: 06 Jun 2018 13:17

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Contributors

Author: A. H. Al-Mazeed
Author: M. S. Nixon ORCID iD
Author: S. R. Gunn
Editor: A. Campilho
Editor: M. Kamel

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