Bring It to the Pitch : Combining Video and Movement Data to Enhance Team Sport Analysis

Thumbnail Image
Date
2018
Editors
Contact
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
DOI (citable link)
ArXiv-ID
International patent number
Link to the license
EU project number
336978
Project
LIA - Light Field Imaging and Analysis
Open Access publication
Restricted until
Title in another language
Research Projects
Organizational Units
Journal Issue
Publication type
Journal article
Publication status
Published
Published in
IEEE transactions on visualization and computer graphics ; 24 (2018), 1. - pp. 13-22. - ISSN 1077-2626. - eISSN 1941-0506
Abstract
Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690STEIN, Manuel, Halldor JANETZKO, Andreas LAMPRECHT, Thorsten BREITKREUTZ, Philip ZIMMERMANN, Bastian GOLDLÜCKE, Tobias SCHRECK, Gennady ANDRIENKO, Michael GROSSNIKLAUS, Daniel A. KEIM, 2018. Bring It to the Pitch : Combining Video and Movement Data to Enhance Team Sport Analysis. In: IEEE transactions on visualization and computer graphics. 24(1), pp. 13-22. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2017.2745181
BibTex
@article{Stein2018-01Bring-41137,
  year={2018},
  doi={10.1109/TVCG.2017.2745181},
  title={Bring It to the Pitch : Combining Video and Movement Data to Enhance Team Sport Analysis},
  number={1},
  volume={24},
  issn={1077-2626},
  journal={IEEE transactions on visualization and computer graphics},
  pages={13--22},
  author={Stein, Manuel and Janetzko, Halldor and Lamprecht, Andreas and Breitkreutz, Thorsten and Zimmermann, Philip and Goldlücke, Bastian and Schreck, Tobias and Andrienko, Gennady and Grossniklaus, Michael and Keim, Daniel A.}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41137">
    <dc:contributor>Zimmermann, Philip</dc:contributor>
    <dc:creator>Andrienko, Gennady</dc:creator>
    <dc:contributor>Janetzko, Halldor</dc:contributor>
    <dc:contributor>Goldlücke, Bastian</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Grossniklaus, Michael</dc:creator>
    <dcterms:title>Bring It to the Pitch : Combining Video and Movement Data to Enhance Team Sport Analysis</dcterms:title>
    <dc:language>eng</dc:language>
    <dc:contributor>Andrienko, Gennady</dc:contributor>
    <dc:creator>Lamprecht, Andreas</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/41137"/>
    <dc:creator>Goldlücke, Bastian</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:creator>Zimmermann, Philip</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Stein, Manuel</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-01-24T15:01:08Z</dc:date>
    <dc:contributor>Breitkreutz, Thorsten</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/41137/1/Stein_2-hr3l95xf6fze7.pdf"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:issued>2018-01</dcterms:issued>
    <dc:contributor>Lamprecht, Andreas</dc:contributor>
    <dc:contributor>Stein, Manuel</dc:contributor>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/41137/1/Stein_2-hr3l95xf6fze7.pdf"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-01-24T15:01:08Z</dcterms:available>
    <dc:creator>Breitkreutz, Thorsten</dc:creator>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dcterms:abstract xml:lang="eng">Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.</dcterms:abstract>
    <dc:contributor>Grossniklaus, Michael</dc:contributor>
    <dc:creator>Janetzko, Halldor</dc:creator>
  </rdf:Description>
</rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Contact
URL of original publication
Test date of URL
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed
Yes