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Improved proposal distribution with gradient measures for tracking

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Improved proposal distribution with gradient measures for tracking. / Brasnett, P.; Mihaylova, L.; Bull, D. et al.
Machine Learning for Signal Processing, 2005 IEEE Workshop on. 2005. p. 105 - 110 .

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

Harvard

Brasnett, P, Mihaylova, L, Bull, D & Canagarajah, N 2005, Improved proposal distribution with gradient measures for tracking. in Machine Learning for Signal Processing, 2005 IEEE Workshop on. pp. 105 - 110 , IEEE International Workshop on Machine Learning for Signal Processing, Mystic, Connecticut, USA, 28/09/05. https://doi.org/10.1109/MLSP.2005.1532883

APA

Brasnett, P., Mihaylova, L., Bull, D., & Canagarajah, N. (2005). Improved proposal distribution with gradient measures for tracking. In Machine Learning for Signal Processing, 2005 IEEE Workshop on (pp. 105 - 110 ) https://doi.org/10.1109/MLSP.2005.1532883

Vancouver

Brasnett P, Mihaylova L, Bull D, Canagarajah N. Improved proposal distribution with gradient measures for tracking. In Machine Learning for Signal Processing, 2005 IEEE Workshop on. 2005. p. 105 - 110 doi: 10.1109/MLSP.2005.1532883

Author

Brasnett, P. ; Mihaylova, L. ; Bull, D. et al. / Improved proposal distribution with gradient measures for tracking. Machine Learning for Signal Processing, 2005 IEEE Workshop on. 2005. pp. 105 - 110

Bibtex

@inproceedings{ea38ceab30a8452f891fdb723960f1e1,
title = "Improved proposal distribution with gradient measures for tracking",
abstract = "Particle filters have become a useful tool for the task of object tracking due to their applicability to a wide range of situations. To be able to obtain an accurate estimate from a particle filter a large number of particles is usually necessary. A crucial step in the design of a particle filter is the choice of the proposal distribution. A common choice for the proposal distribution is to use the transition distribution which models the dynamics of the system but takes no account of the current measurements. We present a particle filter for tracking rigid objects in video sequences that makes use of image gradients in the current frame to improve the proposal distribution. The gradient information is efficiently incorporated in the filter to minimise the computational cost. Results from synthetic and natural sequences show that the gradient information improves the accuracy and reduces the number of particles required.",
keywords = "particle filters, improved proposal distribution, object tracking, video DCS-publications-id, inproc-433, DCS-publications-credits, dsp-fa, DCS-publications-personnel-id, 121",
author = "P. Brasnett and L. Mihaylova and D. Bull and N. Canagarajah",
note = "{"}{\textcopyright}2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.{"} {"}This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"} p. 105-110. doi:10.1109/MLSP.2005.1532883; IEEE International Workshop on Machine Learning for Signal Processing ; Conference date: 28-09-2005 Through 30-09-2005",
year = "2005",
doi = "10.1109/MLSP.2005.1532883",
language = "English",
isbn = "0-7803-9517-4",
pages = "105 -- 110 ",
booktitle = "Machine Learning for Signal Processing, 2005 IEEE Workshop on",

}

RIS

TY - GEN

T1 - Improved proposal distribution with gradient measures for tracking

AU - Brasnett, P.

AU - Mihaylova, L.

AU - Bull, D.

AU - Canagarajah, N.

N1 - "©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." p. 105-110. doi:10.1109/MLSP.2005.1532883

PY - 2005

Y1 - 2005

N2 - Particle filters have become a useful tool for the task of object tracking due to their applicability to a wide range of situations. To be able to obtain an accurate estimate from a particle filter a large number of particles is usually necessary. A crucial step in the design of a particle filter is the choice of the proposal distribution. A common choice for the proposal distribution is to use the transition distribution which models the dynamics of the system but takes no account of the current measurements. We present a particle filter for tracking rigid objects in video sequences that makes use of image gradients in the current frame to improve the proposal distribution. The gradient information is efficiently incorporated in the filter to minimise the computational cost. Results from synthetic and natural sequences show that the gradient information improves the accuracy and reduces the number of particles required.

AB - Particle filters have become a useful tool for the task of object tracking due to their applicability to a wide range of situations. To be able to obtain an accurate estimate from a particle filter a large number of particles is usually necessary. A crucial step in the design of a particle filter is the choice of the proposal distribution. A common choice for the proposal distribution is to use the transition distribution which models the dynamics of the system but takes no account of the current measurements. We present a particle filter for tracking rigid objects in video sequences that makes use of image gradients in the current frame to improve the proposal distribution. The gradient information is efficiently incorporated in the filter to minimise the computational cost. Results from synthetic and natural sequences show that the gradient information improves the accuracy and reduces the number of particles required.

KW - particle filters

KW - improved proposal distribution

KW - object tracking

KW - video DCS-publications-id

KW - inproc-433

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1109/MLSP.2005.1532883

DO - 10.1109/MLSP.2005.1532883

M3 - Conference contribution/Paper

SN - 0-7803-9517-4

SP - 105

EP - 110

BT - Machine Learning for Signal Processing, 2005 IEEE Workshop on

T2 - IEEE International Workshop on Machine Learning for Signal Processing

Y2 - 28 September 2005 through 30 September 2005

ER -