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Multiple object tracking using particle filters

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Multiple object tracking using particle filters. / Jaward, M.; Mihaylova, L.; Canagarajah, N.; Bull, D.

Aerospace Conference, 2006 IEEE. 2006.

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

Harvard

Jaward, M, Mihaylova, L, Canagarajah, N & Bull, D 2006, Multiple object tracking using particle filters. in Aerospace Conference, 2006 IEEE. IEEE Aerospace Conf, Big Sky, MT, USA, 4/03/06. https://doi.org/10.1109/AERO.2006.1655926

APA

Jaward, M., Mihaylova, L., Canagarajah, N., & Bull, D. (2006). Multiple object tracking using particle filters. In Aerospace Conference, 2006 IEEE https://doi.org/10.1109/AERO.2006.1655926

Vancouver

Jaward M, Mihaylova L, Canagarajah N, Bull D. Multiple object tracking using particle filters. In Aerospace Conference, 2006 IEEE. 2006 https://doi.org/10.1109/AERO.2006.1655926

Author

Jaward, M. ; Mihaylova, L. ; Canagarajah, N. ; Bull, D. / Multiple object tracking using particle filters. Aerospace Conference, 2006 IEEE. 2006.

Bibtex

@inproceedings{56254a8840954c26a123b1746a7d871d,
title = "Multiple object tracking using particle filters",
abstract = "The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. In this paper, our recent work [1] on single object tracking using particle filters is extended to multiple objects. In the proposed scheme, track initialisation is embedded in the particle filter without relying on an external object detection scheme. The proposed scheme avoids the use of hybrid state estimation for the estimation of number of active objects and its associated state vectors as proposed in [2]. The number of active objects and track management are handled by means of probabilities of the number of active objects in a given frame. These probabilities are shown to be easily estimated by the Monte Carlo data association algorithm used in our algorithm. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin. The algorithm is able to cope with partial occlusions and to recover the tracks after temporary loss. The probabilities calculated for data associations take part in the calculation of probabilities of the number of objects. We evaluate the performance of the proposed filter on various real-world video sequences with appearing and disappearing targets.",
keywords = "multiple object tracking, particle filtering, data association DCS-publications-id, inproc-437, DCS-publications-credits, dsp, DCS-publications-personnel-id, 121",
author = "M. Jaward and L. Mihaylova and N. Canagarajah and D. Bull",
note = "ISBN: 0-7803-9545-X INSPEC Accession Number: 9110003 Digital Object Identifier: 10.1109/AERO.2006.1655926; IEEE Aerospace Conf ; Conference date: 04-03-2006 Through 11-03-2006",
year = "2006",
month = mar,
day = "5",
doi = "10.1109/AERO.2006.1655926",
language = "English",
isbn = "0-7803-9545-X",
booktitle = "Aerospace Conference, 2006 IEEE",

}

RIS

TY - GEN

T1 - Multiple object tracking using particle filters

AU - Jaward, M.

AU - Mihaylova, L.

AU - Canagarajah, N.

AU - Bull, D.

N1 - ISBN: 0-7803-9545-X INSPEC Accession Number: 9110003 Digital Object Identifier: 10.1109/AERO.2006.1655926

PY - 2006/3/5

Y1 - 2006/3/5

N2 - The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. In this paper, our recent work [1] on single object tracking using particle filters is extended to multiple objects. In the proposed scheme, track initialisation is embedded in the particle filter without relying on an external object detection scheme. The proposed scheme avoids the use of hybrid state estimation for the estimation of number of active objects and its associated state vectors as proposed in [2]. The number of active objects and track management are handled by means of probabilities of the number of active objects in a given frame. These probabilities are shown to be easily estimated by the Monte Carlo data association algorithm used in our algorithm. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin. The algorithm is able to cope with partial occlusions and to recover the tracks after temporary loss. The probabilities calculated for data associations take part in the calculation of probabilities of the number of objects. We evaluate the performance of the proposed filter on various real-world video sequences with appearing and disappearing targets.

AB - The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds. In this paper, our recent work [1] on single object tracking using particle filters is extended to multiple objects. In the proposed scheme, track initialisation is embedded in the particle filter without relying on an external object detection scheme. The proposed scheme avoids the use of hybrid state estimation for the estimation of number of active objects and its associated state vectors as proposed in [2]. The number of active objects and track management are handled by means of probabilities of the number of active objects in a given frame. These probabilities are shown to be easily estimated by the Monte Carlo data association algorithm used in our algorithm. The proposed particle filter (PF) embeds a data association technique based on the joint probabilistic data association (JPDA) which handles the uncertainty of the measurement origin. The algorithm is able to cope with partial occlusions and to recover the tracks after temporary loss. The probabilities calculated for data associations take part in the calculation of probabilities of the number of objects. We evaluate the performance of the proposed filter on various real-world video sequences with appearing and disappearing targets.

KW - multiple object tracking

KW - particle filtering

KW - data association DCS-publications-id

KW - inproc-437

KW - DCS-publications-credits

KW - dsp

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1109/AERO.2006.1655926

DO - 10.1109/AERO.2006.1655926

M3 - Conference contribution/Paper

SN - 0-7803-9545-X

BT - Aerospace Conference, 2006 IEEE

T2 - IEEE Aerospace Conf

Y2 - 4 March 2006 through 11 March 2006

ER -