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Authors: Fedor Taggenbrock 1 ; 2 ; Gertjan Burghouts 1 and Ronald Poppe 2

Affiliations: 1 Utrecht University, Utrecht, Netherlands ; 2 TNO, The Hague, Netherlands

Keyword(s): Self-Supervision, Multi-Camera, Feature Learning, Cycle-Consistency, Cross-View Multi-Object Tracking.

Abstract: Matching objects across partially overlapping camera views is crucial in multi-camera systems and requires a view-invariant feature extraction network. Training such a network with cycle-consistency circumvents the need for labor-intensive labeling. In this paper, we extend the mathematical formulation of cycle-consistency to handle partial overlap. We then introduce a pseudo-mask which directs the training loss to take partial overlap into account. We additionally present several new cycle variants that complement each other and present a time-divergent scene sampling scheme that improves the data input for this self-supervised setting. Cross-camera matching experiments on the challenging DIVOTrack dataset show the merits of our approach. Compared to the self-supervised state-of-the-art, we achieve a 4.3 percentage point higher F1 score with our combined contributions. Our improvements are robust to reduced overlap in the training data, with substantial improvements in challenging s cenes that need to make few matches between many people. Self-supervised feature networks trained with our method are effective at matching objects in a range of multi-camera settings, providing opportunities for complex tasks like large-scale multi-camera scene understanding. (More)

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Paper citation in several formats:
Taggenbrock, F., Burghouts, G. and Poppe, R. (2025). Self-Supervised Partial Cycle-Consistency for Multi-View Matching. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 19-29. DOI: 10.5220/0013080900003912

@conference{visapp25,
author={Fedor Taggenbrock and Gertjan Burghouts and Ronald Poppe},
title={Self-Supervised Partial Cycle-Consistency for Multi-View Matching},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={19-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013080900003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Self-Supervised Partial Cycle-Consistency for Multi-View Matching
SN - 978-989-758-728-3
IS - 2184-4321
AU - Taggenbrock, F.
AU - Burghouts, G.
AU - Poppe, R.
PY - 2025
SP - 19
EP - 29
DO - 10.5220/0013080900003912
PB - SciTePress

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