liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Tracking and data segmentation using a GGIW filter with mixture clustering
Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-3450-988X
Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
Institute of Measurement, Control, and Microtechnology, Ulm UniversityUlm, Germany.
Show others and affiliations
2014 (English)In: FUSION 2014 - 17th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers Inc. , 2014, no 6916137Conference paper, Published paper (Refereed)
Abstract [en]

Common data preprocessing routines often introduce considerable flaws in laser-based tracking of extended objects. As an alternative, extended target tracking methods, such as the Gamma-Gaussian-Inverse Wishart (GGIW) probability hypothesis density (PHD) filter, work directly on raw data. In this paper, the GGIW-PHD filter is applied to real world traffic scenarios. To cope with the large amount of data, a mixture clustering approach which reduces the combinatorial complexity and computation time is proposed. The effective segmentation of raw measurements with respect to spatial distribution and motion is demonstrated and evaluated on two different applications: pedestrian tracking from a vehicle and intersection surveillance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2014. no 6916137
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-116787ISI: 000363896100168Scopus ID: 2-s2.0-84910686579ISBN: 9788490123553 (print)OAI: oai:DiVA.org:liu-116787DiVA: diva2:801606
Conference
17th International Conference on Information Fusion, FUSION 2014
Available from: 2015-04-09 Created: 2015-04-02 Last updated: 2015-12-14

Open Access in DiVA

No full text

Scopus

Authority records BETA

Granström, Karl

Search in DiVA

By author/editor
Granström, Karl
By organisation
Automatic ControlThe Institute of Technology
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 226 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf