Unbalanced Optimal Transport in Multi-Camera Tracking Applications
In: Hyper Article en Ligne - Sciences de l'Homme et de la Société ; International Conference on Pattern Recognition ; International Conference on Pattern Recognition, Jan 2021, Milan, Italy. pp.327-343, ⟨10.1007/978-3-030-68821-9_30⟩, 2021
Online
Konferenz
Zugriff:
International audience 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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Unbalanced Optimal Transport in Multi-Camera Tracking Applications
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Autor/in / Beteiligte Person: | Le, Quoc Cuong ; Conte, Donatello ; Hidane, Moncef ; Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT) ; Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL) ; Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS) |
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Zeitschrift: | Hyper Article en Ligne - Sciences de l'Homme et de la Société ; International Conference on Pattern Recognition ; International Conference on Pattern Recognition, Jan 2021, Milan, Italy. pp.327-343, ⟨10.1007/978-3-030-68821-9_30⟩, 2021 |
Veröffentlichung: | HAL CCSD ; Springer International Publishing, 2021 |
Medientyp: | Konferenz |
DOI: | 10.1007/978-3-030-68821-9_30 |
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