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dc.contributor.authorSerratosa Casanelles, Francesc
dc.contributor.authorAmézquita Gómez, Nicolás
dc.contributor.authorAlquézar Mancho, René
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2010-10-01T13:37:50Z
dc.date.available2010-10-01T13:37:50Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/2117/9245
dc.description.abstractThis paper presents a comparison of two classifiers that are used as a first step within a probabilistic object recognition and tracking framework called PIORT. This first step is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. One of the implemented classifiers is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results show that, on one hand, both classifiers (although they are very different approaches) yield a similar performance when they are integrated within the tracking framework. And on the other hand, our object recognition and tracking framework obtains good results when compared to other published tracking methods in video sequences taken with a moving camera and including total and partial occlusions of the tracked object.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherSpringer Verlag
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject.lcshComputer vision
dc.subject.lcshPattern recognition systems
dc.subject.otherObject tracking Object recognition Occlusion Performance evaluation
dc.titleExperimental assessment of probabilistic integrated object recognition and tracking methods
dc.typeConference report
dc.subject.lemacVisió per ordinador
dc.subject.lemacReconeixement de formes (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.identifier.doi10.1007/978-3-642-10268-4_96
dc.subject.inspecClassificació INSPEC::Pattern recognition::Computer vision
dc.subject.inspecClassificació INSPEC::Pattern recognition::Object detection
dc.subject.inspecClassificació INSPEC::Pattern recognition
dc.relation.publisherversionhttp://dx.doi.org/10.1007/978-3-642-10268-4_96
dc.rights.accessOpen Access
drac.iddocument2543773
dc.description.versionPreprint


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