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Box-particle intensity filter

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Box-particle intensity filter. / Schikora, Marek; Gning, Amadou; Mihaylova, Lyudmila et al.
Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. 2012.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Schikora, M, Gning, A, Mihaylova, L, Cremers, D, Koch, W & Streit, R 2012, Box-particle intensity filter. in Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications, United Kingdom, 16/05/12. https://doi.org/10.1049/cp.2012.0405

APA

Schikora, M., Gning, A., Mihaylova, L., Cremers, D., Koch, W., & Streit, R. (2012). Box-particle intensity filter. In Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET https://doi.org/10.1049/cp.2012.0405

Vancouver

Schikora M, Gning A, Mihaylova L, Cremers D, Koch W, Streit R. Box-particle intensity filter. In Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. 2012 doi: 10.1049/cp.2012.0405

Author

Schikora, Marek ; Gning, Amadou ; Mihaylova, Lyudmila et al. / Box-particle intensity filter. Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET. 2012.

Bibtex

@inproceedings{9eacffbbd4ee4892803d4514f2eb3989,
title = "Box-particle intensity filter",
abstract = "This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty.The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.",
keywords = "Multi-Target Tracking, Box Particle Filters, Poisson Point Processes, Intensity Filter, interval measurements",
author = "Marek Schikora and Amadou Gning and Lyudmila Mihaylova and Daniel Cremers and Wofgang Koch and Roy Streit",
year = "2012",
month = may,
day = "15",
doi = "10.1049/cp.2012.0405",
language = "English",
booktitle = "Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET",
note = "The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications ; Conference date: 16-05-2012 Through 17-05-2012",

}

RIS

TY - GEN

T1 - Box-particle intensity filter

AU - Schikora, Marek

AU - Gning, Amadou

AU - Mihaylova, Lyudmila

AU - Cremers, Daniel

AU - Koch, Wofgang

AU - Streit, Roy

PY - 2012/5/15

Y1 - 2012/5/15

N2 - This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty.The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

AB - This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty.The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

KW - Multi-Target Tracking

KW - Box Particle Filters

KW - Poisson Point Processes

KW - Intensity Filter

KW - interval measurements

U2 - 10.1049/cp.2012.0405

DO - 10.1049/cp.2012.0405

M3 - Conference contribution/Paper

BT - Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET

T2 - The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications

Y2 - 16 May 2012 through 17 May 2012

ER -