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  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.

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Genre: Conference Paper
Latex : {LMGP}: {L}ifted Multicut Meets Geometry Projections for Multi-Camera Multi-Object Tracking

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arXiv:2111.11892.pdf (Preprint), 21MB
 
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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.
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 Creators:
Nguyen, Duy H. M.1, Author           
Henschel, Roberto2, Author
Rosenhahn, Bodo2, Author
Sonntag, Daniel2, Author
Swoboda, Paul1, Author           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Abstract: 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.

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Language(s): eng - English
 Dates: 2021-11-232022
 Publication Status: Published online
 Pages: 16 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Nguyen_CVPR22
DOI: 10.1109/CVPR52688.2022.00866
 Degree: -

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Title: 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
Place of Event: New Orleans, LA, USA
Start-/End Date: 2022-06-19 - 2022-06-24

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Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2022
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 8856 - 8865 Identifier: ISBN: 978-1-6654-6946-3