Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion

Amirali K. Gostar; Tharindu Rathnayake; Ruwan Tennakoon; Alireza Bab-Haidashar; Reza Hoseinnezhad

doi:10.23919/ICIF.2018.8455726

Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion

Amirali K. Gostar
, Tharindu Rathnayake
, Ruwan Tennakoon
, Alireza Bab-Haidashar
, Reza Hoseinnezhad

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

2 Citations (Scopus)

Abstract

In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.

Original language	English
Title of host publication	2018 21st International Conference on Information Fusion, FUSION 2018
Editors	Daniel Clark, Roland Hostettler, Peter Willett
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	289-294
Number of pages	6
ISBN (Electronic)	9780996452779, 9781538643303
ISBN (Print)	9780996452762
DOIs	https://doi.org/10.23919/ICIF.2018.8455726
Publication status	Published - 2018
Externally published	Yes
Event	International Conference on Information Fusion 2018 - Cambridge, United Kingdom Duration: 10 Jul 2018 → 13 Jul 2018 Conference number: 21st https://ieeexplore.ieee.org/xpl/conhome/8442112/proceeding (Proceedings) https://fusion2018.eng.cam.ac.uk (Website)

Conference

Conference	International Conference on Information Fusion 2018
Abbreviated title	FUSION 2018
Country/Territory	United Kingdom
City	Cambridge
Period	10/07/18 → 13/07/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8442112/proceeding (Proceedings) https://fusion2018.eng.cam.ac.uk (Website)

Keywords

cell tracking
Finite set statistics
Poisson random finite set
random set theory
track-before-detect

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10.23919/ICIF.2018.8455726

Cite this

Gostar, A. K., Rathnayake, T., Tennakoon, R., Bab-Haidashar, A., & Hoseinnezhad, R. (2018). Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion. In D. Clark, R. Hostettler, & P. Willett (Eds.), 2018 21st International Conference on Information Fusion, FUSION 2018 (pp. 289-294). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/ICIF.2018.8455726

Gostar, Amirali K. ; Rathnayake, Tharindu ; Tennakoon, Ruwan et al. / Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion. 2018 21st International Conference on Information Fusion, FUSION 2018. editor / Daniel Clark ; Roland Hostettler ; Peter Willett. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 289-294

@inproceedings{9ea4e9dadcee4da98d55e0968adb4bcb,

title = "Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion",

abstract = "In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.",

keywords = "cell tracking, Finite set statistics, Poisson random finite set, random set theory, track-before-detect",

author = "Gostar, \{Amirali K.\} and Tharindu Rathnayake and Ruwan Tennakoon and Alireza Bab-Haidashar and Reza Hoseinnezhad",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Australian Research Council via its Discovery Program grant DP160104662. Publisher Copyright: {\textcopyright} 2018 ISIF; International Conference on Information Fusion 2018, FUSION 2018 ; Conference date: 10-07-2018 Through 13-07-2018",

year = "2018",

doi = "10.23919/ICIF.2018.8455726",

language = "English",

isbn = "9780996452762",

pages = "289--294",

editor = "Daniel Clark and Roland Hostettler and Peter Willett",

booktitle = "2018 21st International Conference on Information Fusion, FUSION 2018",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/8442112/proceeding, https://fusion2018.eng.cam.ac.uk",

}

Gostar, AK, Rathnayake, T, Tennakoon, R, Bab-Haidashar, A & Hoseinnezhad, R 2018, Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion. in D Clark, R Hostettler & P Willett (eds), 2018 21st International Conference on Information Fusion, FUSION 2018. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 289-294, International Conference on Information Fusion 2018, Cambridge, United Kingdom, 10/07/18. https://doi.org/10.23919/ICIF.2018.8455726

Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion. / Gostar, Amirali K.; Rathnayake, Tharindu; Tennakoon, Ruwan et al.
2018 21st International Conference on Information Fusion, FUSION 2018. ed. / Daniel Clark; Roland Hostettler; Peter Willett. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 289-294.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion

AU - Gostar, Amirali K.

AU - Rathnayake, Tharindu

AU - Tennakoon, Ruwan

AU - Bab-Haidashar, Alireza

AU - Hoseinnezhad, Reza

N1 - Conference code: 21st

PY - 2018

Y1 - 2018

N2 - In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.

AB - In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.

KW - cell tracking

KW - Finite set statistics

KW - Poisson random finite set

KW - random set theory

KW - track-before-detect

UR - https://www.scopus.com/pages/publications/85054084772

U2 - 10.23919/ICIF.2018.8455726

DO - 10.23919/ICIF.2018.8455726

M3 - Conference Paper

AN - SCOPUS:85054084772

SN - 9780996452762

SP - 289

EP - 294

BT - 2018 21st International Conference on Information Fusion, FUSION 2018

A2 - Clark, Daniel

A2 - Hostettler, Roland

A2 - Willett, Peter

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference on Information Fusion 2018

Y2 - 10 July 2018 through 13 July 2018

ER -

Gostar AK, Rathnayake T, Tennakoon R, Bab-Haidashar A, Hoseinnezhad R. Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion. In Clark D, Hostettler R, Willett P, editors, 2018 21st International Conference on Information Fusion, FUSION 2018. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 289-294 doi: 10.23919/ICIF.2018.8455726

Non-bayesian track-before-detect using Cauchy-Schwarz divergence-based information fusion

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Keywords

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