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dc.contributor.authorAgudo Martínez, Antonio
dc.contributor.authorLepetit, Vincent
dc.contributor.authorMoreno-Noguer, Francesc
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2022-04-11T10:43:16Z
dc.date.available2022-04-11T10:43:16Z
dc.date.issued2021-07-19
dc.identifier.citationAgudo, A.; Lepetit, V.; Moreno-Noguer, F. Simultaneous completion and spatiotemporal grouping of corrupted motion tracks. "Visual computer", 19 Juliol 2021,
dc.identifier.issn1432-2315
dc.identifier.urihttp://hdl.handle.net/2117/365657
dc.description.abstractGiven an unordered list of 2D or 3D point trajectories corrupted by noise and partial observations, in this paper we introduce a framework to simultaneously recover the incomplete motion tracks and group the points into spatially and temporally coherent clusters. This advances existing work, which only addresses partial problems and without considering a unified and unsupervised solution. We cast this problem as a matrix completion one, in which point tracks are arranged into a matrix with the missing entries set as zeros. In order to perform the double clustering, the measurement matrix is assumed to be drawn from a dual union of spatiotemporal subspaces. The bases and the dimensionality for these subspaces, the affinity matrices used to encode the temporal and spatial clusters to which each point belongs, and the non-visible tracks, are then jointly estimated via augmented Lagrange multipliers in polynomial time. A thorough evaluation on incomplete motion tracks for multiple-object typologies shows that the accuracy of the matrix we recover compares favorably to that obtained with existing low-rank matrix completion methods, specially under noisy measurements. In addition, besides recovering the incomplete tracks, the point trajectories are directly grouped into different object instances, and a number of semantically meaningful temporal primitive actions are automatically discovered
dc.description.sponsorshipThis work has been partially supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI MDM-2016-0656, by the Spanish Ministry of Science and Innovation under project HuMoUR TIN2017-90086-R and the Salvador de Madariaga grant PRX19/00626, and by the ERA-net CHIST-ERA project IPALM PCI2019-103386.
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshComputer vision
dc.subject.otherPoint track completion
dc.subject.otherSpatiotemporal clustering
dc.subject.otherAugmented Lagrangian multiplier
dc.titleSimultaneous completion and spatiotemporal grouping of corrupted motion tracks
dc.typeArticle
dc.subject.lemacVisió per ordinador
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.identifier.doi10.1007/s00371-021-02238-8
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/article/10.1007%2Fs00371-021-02238-8
dc.rights.accessOpen Access
local.identifier.drac32061990
dc.description.versionPostprint (published version)
local.citation.authorAgudo, A.; Lepetit, V.; Moreno-Noguer, F.
local.citation.publicationNameVisual computer


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Attribution 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 3.0 Spain