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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Unbalanced Optimal Transport in Multi-camera Tracking Applications

verfasst von : Quoc Cuong Le, Donatello Conte, Moncef Hidane

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

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Abstract

Multi-view multi-object tracking algorithms are expected to resolve multi-object tracking persistent issues within a single camera. However, the inconsistency of camera videos in most of the surveillance systems obstructs the ability of re-identifying and jointly tracking targets through different views. As a crucial task in multi-camera tracking, assigning targets from one view to another is considered as an assignment problem. This paper is presenting an alternative approach based on Unbalanced Optimal Transport for the unbalanced assignment problem. On each view, targets’ position and appearance are projected on a learned metric space, and then an Unbalanced Optimal Transport algorithm is applied to find the optimal assignment of targets between pairs of views. The experiments on common multi-camera databases show the superiority of our proposal to the heuristic approach on MOT metrics.

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Metadaten
Titel
Unbalanced Optimal Transport in Multi-camera Tracking Applications
verfasst von
Quoc Cuong Le
Donatello Conte
Moncef Hidane
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68820-2

Electronic ISBN: 978-3-030-68821-9

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68821-9_30