INCREMENTAL LEARNING OF EVENTS IN VIDEO USING RELIABLE INFORMATION

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INCREMENTAL LEARNING OF EVENTS IN VIDEO USING RELIABLE INFORMATION

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dc.contributor.advisor THONNAT, MONIQUE
dc.creator ZUÑIGA, MARCOS
dc.date.accessioned 2014-08-11T19:22:45Z
dc.date.available 2014-08-11T19:22:45Z
dc.date.copyright 2008
dc.date.issued 2014-08-11
dc.identifier.uri http://hdl.handle.net/10533/90956
dc.description.abstract The goal of this thesis is to propose a general video understanding framework for learning and recognition of events occurring in videos, for real world applications. This video understanding framework is composed of four tasks: First, at each video frame, a segmentation task detects the moving regions, represented by bounding boxes enclosing them. Second, a new 3D classifier associates to each moving region an object class label (e.g. person, vehicle) and a 3D parallelepiped described by its width, height, length, position, orientation, and visual reliability measures of these attributes. Third, a new multi-object tracking algorithm uses these object descriptions to generate tracking hypotheses about the objects evolving in the scene. Reliability measures associated to the object features are used to perform a proper selection of valuable information. Finally, a new incremental event learning algorithm aggregates on-line the attributes and reliability information of the tracked objects to learn a hierarchy of concepts describing the events occurring in the scene. Reliability measures are used to focus the learning process on the most valuable information. Simultaneously, the event learning approach recognises the events associated to the objects evolving in the scene. The tracking approach has been validated using video-surveillance benchmarks publicly accessible. The complete video understanding framework has been evaluated with videos for a real elderly care application. The framework has been able to successfully learn events related to trajectory (e.g. change in 3D position and velocity), posture (e.g. standing up, crouching), and object interaction (e.g. person approaching to a table), among other events, with a minimal configuration effort.
dc.language.iso eng
dc.rights Atribución-NoComercial-SinDerivadas 3.0 Chile
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.source Repositorio Digital Comisión Nacional de Investigación Ciencia y Tecnología -CONICYT-Chile
dc.title INCREMENTAL LEARNING OF EVENTS IN VIDEO USING RELIABLE INFORMATION
dc.type Tesis Doctorado
dc.subject.OECD OTRAS INGENIERIAS Y TECNOLOGIAS
dc.country FRANCIA
dc.description.degree DOCTOR EN CIENCIAS ESPECIALIDAD INFORMATICA
dc.subject.fondecyt INFORMATICA
dc.identifier.scholarship Doctorado INRIA
dc.contributor.institution ABERYSTWYTH UNIVERSITY
dc.type.driver info:eu-repo/semantics/doctoralThesis
dc.rights.driver info:eu-repo/semantics/openAcces

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