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Evolving networks for group object motion estimation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published

Standard

Evolving networks for group object motion estimation. / Gning, A.; Mihaylova, L.; Maskell, Simon; Pang, Sze Kim; Godsill, Simon J.

IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008. IEEE, 2008. p. 99-106.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Gning, A, Mihaylova, L, Maskell, S, Pang, SK & Godsill, SJ 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, Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications, Birmingham, UK, 15/04/08. <http://www.theiet.org/target>

APA

Gning, A., Mihaylova, L., Maskell, S., Pang, S. K., & Godsill, S. J. (2008). Evolving networks for group object motion estimation. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 (pp. 99-106). IEEE. http://www.theiet.org/target

Vancouver

Gning A, Mihaylova L, Maskell S, Pang SK, Godsill SJ. Evolving networks for group object motion estimation. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008. IEEE. 2008. p. 99-106

Author

Gning, A. ; Mihaylova, L. ; Maskell, Simon ; Pang, Sze Kim ; Godsill, Simon J. / Evolving networks for group object motion estimation. IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008. IEEE, 2008. pp. 99-106

Bibtex

@inproceedings{0e0f5cb211f44f4691b51e401427cbba,
title = "Evolving networks for group object motion estimation",
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.",
keywords = "evolving graphs, random graphs, group target tracking, Monte Carlo methods, nonlinear estimation ",
author = "A. Gning and L. Mihaylova and Simon Maskell and Pang, {Sze Kim} and Godsill, {Simon J.}",
note = "pp. 99-106 ISBN 9780863419102 ISSN 0537-9989 Reference PES08273; Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications ; Conference date: 15-04-2008 Through 16-04-2008",
year = "2008",
month = apr,
day = "16",
language = "English",
isbn = "978-0-86341-910-2",
pages = "99--106",
booktitle = "IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Evolving networks for group object motion estimation

AU - Gning, A.

AU - Mihaylova, L.

AU - Maskell, Simon

AU - Pang, Sze Kim

AU - Godsill, Simon J.

N1 - pp. 99-106 ISBN 9780863419102 ISSN 0537-9989 Reference PES08273

PY - 2008/4/16

Y1 - 2008/4/16

N2 - 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.

AB - 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.

KW - evolving graphs

KW - random graphs

KW - group target tracking

KW - Monte Carlo methods

KW - nonlinear estimation

M3 - Conference contribution/Paper

SN - 978-0-86341-910-2

SP - 99

EP - 106

BT - IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008

PB - IEEE

T2 - Institution of Engineering and Technology (IET) Seminar on Target Tracking and Data Fusion: Algorithms and Applications

Y2 - 15 April 2008 through 16 April 2008

ER -