Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 Lokale TagsFreigabegeschichteDetailsÜbersicht
  LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

Nguyen, D. H. M., Henschel, R., Rosenhahn, B., Sonntag, D., & Swoboda, P. (2022). LMGP: Lifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8856-8865). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.00866.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0009-B3ED-2 Versions-Permalink: https://hdl.handle.net/21.11116/0000-000C-13C4-1
Genre: Konferenzbeitrag
Latex : {LMGP}: {L}ifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

Dateien

einblenden: Dateien
ausblenden: Dateien
:
arXiv:2111.11892.pdf (Preprint), 21MB
 
Datei-Permalink:
-
Name:
arXiv:2111.11892.pdf
Beschreibung:
File downloaded from arXiv at 2022-01-04 08:43
OA-Status:
Sichtbarkeit:
Privat
MIME-Typ / Prüfsumme:
application/pdf
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
http://creativecommons.org/licenses/by/4.0/
:
Nguyen_LMGP_Lifted_Multicut_Meets_Geometry_Projections_for_Multi-Camera_Multi-Object_Tracking_CVPR_2022_paper.pdf (Preprint), 7MB
Öffnen   Speichern
Datei-Permalink:
https://hdl.handle.net/21.11116/0000-000C-13C5-0
Name:
Nguyen_LMGP_Lifted_Multicut_Meets_Geometry_Projections_for_Multi-Camera_Multi-Object_Tracking_CVPR_2022_paper.pdf
Beschreibung:
-
OA-Status:
Grün
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Öffnen
Copyright Datum:
-
Copyright Info:
These CVPR 2021 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. © 2021 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.
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Nguyen, Duy H. M.1, Autor           
Henschel, Roberto2, Autor
Rosenhahn, Bodo2, Autor
Sonntag, Daniel2, Autor
Swoboda, Paul1, Autor           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Zusammenfassung: Multi-Camera Multi-Object Tracking is currently drawing attention in the
computer vision field due to its superior performance in real-world
applications such as video surveillance with crowded scenes or in vast space.
In this work, we propose a mathematically elegant multi-camera multiple object
tracking approach based on a spatial-temporal lifted multicut formulation. Our
model utilizes state-of-the-art tracklets produced by single-camera trackers as
proposals. As these tracklets may contain ID-Switch errors, we refine them
through a novel pre-clustering obtained from 3D geometry projections. As a
result, we derive a better tracking graph without ID switches and more precise
affinity costs for the data association phase. Tracklets are then matched to
multi-camera trajectories by solving a global lifted multicut formulation that
incorporates short and long-range temporal interactions on tracklets located in
the same camera as well as inter-camera ones. Experimental results on the
WildTrack dataset yield near-perfect result, outperforming state-of-the-art
trackers on Campus while being on par on the PETS-09 dataset. We will make our
implementations available upon acceptance of the paper.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2021-11-232022
 Publikationsstatus: Online veröffentlicht
 Seiten: 16 p.
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: Nguyen_CVPR22
DOI: 10.1109/CVPR52688.2022.00866
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
Veranstaltungsort: New Orleans, LA, USA
Start-/Enddatum: 2022-06-19 - 2022-06-24

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  Kurztitel : CVPR 2022
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Piscataway, NJ : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 8856 - 8865 Identifikator: ISBN: 978-1-6654-6946-3