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dc.contributorCamps, Octavia
dc.contributorGiró Nieto, Xavier
dc.contributor.authorAlonso Poal, Marina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2020-07-29T08:58:59Z
dc.date.available2020-07-29T08:58:59Z
dc.date.issued2020-06-02
dc.identifier.urihttp://hdl.handle.net/2117/327953
dc.descriptionTo be defined upon arrival.
dc.description.abstractThe task of Video Object Tracking has for a long time received attention within the field of Computer Vision, and many different approaches have tried to tackle its challenges, being the ones based on appearance and motion some of the most popular ones. The main focus of this thesis is to fuse both strategies in order to exploit their strengths and overcome each other's flaws. To achieve this goal, we propose a unified framework that combines, in an online manner, an off-the-shelf single-object siamese tracker, which is modified to perform multi-object tracking and to provide more than one detection candidate, with a novel motion module. This module detects when the proposed target position is not dynamically consistent and, if that is the case, predicts an alternative which is used to choose the best among the rest of candidates. Our approach is evaluated on the challenging Similar Multi-Object Tracking (SMOT) dataset and achieves a relevant precision improvement of the 5% with respect to the baseline. We present an extension to the SMOT dataset, the eSMOT, including more sequences with complex dynamic scenarios, where the performance of our model is excellent, therefore we use its predictions to label the Ground Truth. Although there is still room for enhancement mainly regarding the efficiency of the approach, this work has served as a relevant proof of concept for the intuitions behind it and consequently, research in this direction will surely continue.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsS'autoritza la difusió de l'obra mitjançant la llicència Creative Commons o similar 'Reconeixement-NoComercial- SenseObraDerivada'
dc.subject.othervideo object tracking
dc.subject.otherdynamics theory
dc.titleExploring Methods to Enhance Appearance-Based Video Object Tracking using Dynamics Theory
dc.title.alternativeExploring Methods to Enhance Appearance-Based Video Object Tracking using Dynamics Theory
dc.typeMaster thesis
dc.identifier.slugETSETB-230.149809
dc.rights.accessOpen Access
dc.date.updated2020-07-29T05:52:49Z
dc.audience.educationlevelMàster
dc.audience.mediatorEscola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona
dc.audience.degreeMÀSTER UNIVERSITARI EN TECNOLOGIES AVANÇADES DE TELECOMUNICACIÓ (Pla 2019)
dc.contributor.covenanteeNortheastern University


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