Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter

Mark Campbell; Daniel E. Clark

doi:10.1109/SDF.2019.8916653

Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter

Mark Campbell, Daniel E. Clark

School of Engineering & Physical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

37 Downloads (Pure)

Abstract

The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.

Original language	English
Title of host publication	2019 Sensor Data Fusion
Subtitle of host publication	Trends, Solutions, Applications (SDF)
Publisher	IEEE
ISBN (Electronic)	9781728150857
DOIs	https://doi.org/10.1109/SDF.2019.8916653
Publication status	Published - 28 Nov 2019
Event	2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications - Bonn, Germany Duration: 15 Oct 2019 → 17 Oct 2019

Publication series

Name	Symposium on Sensor Data Fusion
ISSN (Print)	2333-7427

Conference

Conference	2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications
Abbreviated title	SDF 2019
Country/Territory	Germany
City	Bonn
Period	15/10/19 → 17/10/19

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Instrumentation

Access to Document

10.1109/SDF.2019.8916653

Download pdf
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 710 KB

Cite this

@inproceedings{9c4a0f8a00314c38a00bc6df20acc9cb,

title = "Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter",

abstract = "The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.",

author = "Mark Campbell and Clark, {Daniel E.}",

year = "2019",

month = nov,

day = "28",

doi = "10.1109/SDF.2019.8916653",

language = "English",

series = "Symposium on Sensor Data Fusion",

publisher = "IEEE",

booktitle = "2019 Sensor Data Fusion",

address = "United States",

note = "2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2019 ; Conference date: 15-10-2019 Through 17-10-2019",

}

Campbell, M & Clark, DE 2019, Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter. in 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)., 8916653, Symposium on Sensor Data Fusion, IEEE, 2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications, Bonn, Germany, 15/10/19. https://doi.org/10.1109/SDF.2019.8916653

TY - GEN

T1 - Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter

AU - Campbell, Mark

AU - Clark, Daniel E.

PY - 2019/11/28

Y1 - 2019/11/28

N2 - The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.

AB - The calibration of an unknown sensor, such as a camera, is a key issue in the sensor fusion domain. This paper addresses this problem by expanding upon previously introduced work. This method uses a unified Bayesian framework with an alternative parameterisation known as disparity space to calibrate an unknown camera's spatial parameters in reference to a known camera. Here, the recently developedLinear-Complexity Cumulant (LCC) filter is used to improve the both the multitarget tracking and calibration facets of the framework. The new implementation is compared against a Probability Hypothesis Density (PHD) method upon simulated data.

UR - http://www.scopus.com/inward/record.url?scp=85076926648&partnerID=8YFLogxK

U2 - 10.1109/SDF.2019.8916653

DO - 10.1109/SDF.2019.8916653

M3 - Conference contribution

T3 - Symposium on Sensor Data Fusion

BT - 2019 Sensor Data Fusion

PB - IEEE

T2 - 2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications

Y2 - 15 October 2019 through 17 October 2019

ER -

Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this