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Evolving networks for group object motion estimation

Gning, A. and Mihaylova, L. and Maskell, Simon and Pang, Sze Kim and Godsill, Simon J. (2008) Evolving networks for group object motion estimation. In: IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008. IEEE, pp. 99-106. ISBN 978-0-86341-910-2

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Abstract

This paper proposes a technique for group object motion estimation based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the group. An algorithm is proposed for automatic graph structure initialisation, incorporation of new nodes and unexisting nodes removal in parallel with the edge update. This evolving graph model is combined with the sequential Monte Carlo framework and its effectiveness is illustrated over a complex scenario for group motion estimation in urban environment. Results with merging, splitting and crossing of the groups are presented with high estimation accuracy.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: pp. 99-106 ISBN 9780863419102 ISSN 0537-9989 Reference PES08273
Uncontrolled Keywords: evolving graphs ; random graphs ; group target tracking ; Monte Carlo methods ; nonlinear estimation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 8319
Deposited By: Dr L Mihaylov
Deposited On: 18 Apr 2008 08:51
Refereed?: No
Published?: Published
Last Modified: 21 Oct 2017 01:29
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/8319

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