Hierarchical Global Tracking Hypotheses Generator

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dc.contributor.author Gómez Silva, María José
dc.contributor.author Escalera Hueso, Arturo de la
dc.contributor.author Armingol Moreno, José María
dc.date.accessioned 2022-11-29T10:02:34Z
dc.date.issued 2022-11-11
dc.identifier.bibliographicCitation Gómez-Silva, M. J., de la Escalera, A. & Armingol, J. M. (2022). Hierarchical generator of tracking global hypotheses. Expert Systems with Applications, 206, 117813.
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10016/36121
dc.description.abstract The presence of crowds, crossing people, occlusions, and individuals entering and leaving the monitored scenario turns the automatization of Multi-Object Tracking into a demanding task. Due to the difficulties in dealing with those situations, the data association between the incoming observations and their corresponding identities could produce split, merged, and even missed tracks. This article proposes a Hierarchical Generator of Tracking Global Hypotheses (HGTGH) to prevent those errors. In this method, the data association process is divided into hierarchical levels according to multiple factors, such as the duration of tracking on the individuals or the number of frames in a row where they have been missed. A dedicated formulation of the association cost at each level properly combines various affinity metrics. Instead of generating hypotheses for each individual and analyzing them through a batch of future frames, the proposed method immediately generates a global hypothesis that describes the assignment of a whole set of identities on every incoming frame. The generated hypothesis is also able to render new people entering the scene. Thanks to this advantage, the proposed method simultaneously addresses the reduction of identity switches and the problem of starting new tracks. This novel data association method constitutes the core of an online tracking algorithm, which has been evaluated over the MOT17 dataset to demonstrate its effectiveness.
dc.description.sponsorship This work was supported by the Spanish Government through the CICYT projects [grant numbers: TRA2016-78886-C3-1-R, RTI2018-096036-B-C21], Universidad Carlos III of Madrid through (PEAVAUTO-CM-UC3M) and the Comunidad de Madrid through SEGVAUTO-4.0-CM [grant number: P2018/EMT-4362] and Ministerio de Educación, Cultura y Deporte para la Formación de Profesorado Universitario [grant number: FPU14/02143]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.
dc.format.extent 15
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2022 Elsevier Ltd. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Multi-object tracking
dc.subject.other Hierarchical data association
dc.subject.other Tracking global hypothesis
dc.subject.other Appearance neural model
dc.title Hierarchical Global Tracking Hypotheses Generator
dc.type research article
dc.subject.eciencia Ingeniería Mecánica
dc.identifier.doi https://doi.org/10.1016/j.eswa.2022.117813
dc.rights.accessRights embargoed access
dc.relation.projectID Gobierno de España. TRA2016-78886-C3-1-R
dc.relation.projectID Gobierno de España. RTI2018-096036-B-C21
dc.relation.projectID Universidad Carlos III de Madrid. PEAVAUTO-CM-UC3M
dc.relation.projectID Comunidad de Madrid. P2018/EMT-4362
dc.relation.projectID Gobierno de Españaa. FPU14/02143
dc.identifier.publicationfirstpage 1
dc.identifier.publicationlastpage 15
dc.identifier.publicationtitle Expery Systems with Applications
dc.identifier.publicationvolume 206
dc.identifier.uxxi AR/0000027619
carlosiii.embargo.liftdate 2024-11-15
carlosiii.embargo.terms 2024-11-15
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
dc.contributor.funder Ministerio de Educación, Cultura y Deporte (España)
dc.contributor.funder Universidad Carlos III de Madrid
dc.affiliation.dpto UC3M. Departamento de Ingeniería de Sistemas y Automática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligentes
dc.type.hasVersion AM
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