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

Mark Campbell, Daniel E. Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 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 languageEnglish
Title of host publication2019 Sensor Data Fusion
Subtitle of host publicationTrends, Solutions, Applications (SDF)
PublisherIEEE
ISBN (Electronic)9781728150857
DOIs
Publication statusPublished - 28 Nov 2019
Event2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications - Bonn, Germany
Duration: 15 Oct 201917 Oct 2019

Publication series

NameSymposium on Sensor Data Fusion
ISSN (Print)2333-7427

Conference

Conference2019 Symposium on Sensor Data Fusion: Trends, Solutions, Applications
Abbreviated titleSDF 2019
CountryGermany
CityBonn
Period15/10/1917/10/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Instrumentation

Fingerprint Dive into the research topics of 'Joint stereo camera calibration and multi-target tracking using the linear-complexity factorial cumulant filter'. Together they form a unique fingerprint.

Cite this