Computer vision meets visual analytics : enabling 4D crime scene investigation from image and video data

Lade...
Vorschaubild
Dateien
Pollok_2-85pliwhm9xiw8.pdf
Pollok_2-85pliwhm9xiw8.pdfGröße: 462.14 KBDownloads: 66
Datum
2019
Autor:innen
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019). Piscataway, NJ: IEEE, 2019, pp. 44-49. ISBN 978-1-83953-109-5. Available under: doi: 10.1049/cp.2019.1166
Zusammenfassung

In case of a crime or terrorist attack, nowadays much video footage is available from surveillance and mobile cameras recorded by witnesses. While immediate results can be crucial for the prevention of further incidents, the investigation of such events is typically very costly due to the human resources and time that are needed to process the mass data for an investigation. In this paper, we present an approach that creates a 4D reconstruction from mass data, which is a spatio-temporal reconstruction computed from all available images and video footage. The resulting 4D reconstruction gives investigators an intuitive overview of all camera locations and their viewing directions. It provides investigators the ability to view the original video or image footage at any specific point in time. Combined with an innovative 4D interface, our resulting 4D reconstruction enables investigators to view a crime scene in a way that is similar to watching a video where one can freely navigate in space and time. Furthermore, our approach augments the scene with automatic detections and their trajectories and enrich the crime scene with annotations serving as clues.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
4D Reconstruction, Computer Vision, Crime Scene Investigation, Forensics, Visual Exploration
Konferenz
9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019), 16. Dez. 2019 - 18. Dez. 2019, London, UK
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690POLLOK, Thomas, Matthias KRAUS, Chenghao QU, Matthias MILLER, Tobias MORITZ, Timon KILIAN, Daniel A. KEIM, Wolfgang JENTNER, 2019. Computer vision meets visual analytics : enabling 4D crime scene investigation from image and video data. 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019). London, UK, 16. Dez. 2019 - 18. Dez. 2019. In: 9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019). Piscataway, NJ: IEEE, 2019, pp. 44-49. ISBN 978-1-83953-109-5. Available under: doi: 10.1049/cp.2019.1166
BibTex
@inproceedings{Pollok2019Compu-66538,
  year={2019},
  doi={10.1049/cp.2019.1166},
  title={Computer vision meets visual analytics : enabling 4D crime scene investigation from image and video data},
  isbn={978-1-83953-109-5},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={9th International Conference on Imaging for Crime Detection and Prevention (ICDP-2019)},
  pages={44--49},
  author={Pollok, Thomas and Kraus, Matthias and Qu, Chenghao and Miller, Matthias and Moritz, Tobias and Kilian, Timon and Keim, Daniel A. and Jentner, Wolfgang}
}
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/123456789/66538">
    <dc:contributor>Kilian, Timon</dc:contributor>
    <dcterms:title>Computer vision meets visual analytics : enabling 4D crime scene investigation from image and video data</dcterms:title>
    <dcterms:abstract>In case of a crime or terrorist attack, nowadays much video footage is available from surveillance and mobile cameras recorded by witnesses. While immediate results can be crucial for the prevention of further incidents, the investigation of such events is typically very costly due to the human resources and time that are needed to process the mass data for an investigation. In this paper, we present an approach that creates a 4D reconstruction from mass data, which is a spatio-temporal reconstruction computed from all available images and video footage. The resulting 4D reconstruction gives investigators an intuitive overview of all camera locations and their viewing directions. It provides investigators the ability to view the original video or image footage at any specific point in time. Combined with an innovative 4D interface, our resulting 4D reconstruction enables investigators to view a crime scene in a way that is similar to watching a video where one can freely navigate in space and time. Furthermore, our approach augments the scene with automatic detections and their trajectories and enrich the crime scene with annotations serving as clues.</dcterms:abstract>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66538/1/Pollok_2-85pliwhm9xiw8.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-04-05T07:13:41Z</dcterms:available>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:issued>2019</dcterms:issued>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:creator>Miller, Matthias</dc:creator>
    <dc:creator>Qu, Chenghao</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/66538"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Moritz, Tobias</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/66538/1/Pollok_2-85pliwhm9xiw8.pdf"/>
    <dc:creator>Moritz, Tobias</dc:creator>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:language>eng</dc:language>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-04-05T07:13:41Z</dc:date>
    <dc:contributor>Jentner, Wolfgang</dc:contributor>
    <dc:contributor>Kraus, Matthias</dc:contributor>
    <dc:creator>Kraus, Matthias</dc:creator>
    <dc:contributor>Qu, Chenghao</dc:contributor>
    <dc:creator>Kilian, Timon</dc:creator>
    <dc:contributor>Pollok, Thomas</dc:contributor>
    <dc:creator>Jentner, Wolfgang</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Miller, Matthias</dc:contributor>
    <dc:creator>Pollok, Thomas</dc:creator>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen