Bilattice based Logical Reasoning for Automated Visual Surveillance and other Applications

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dc.contributor.advisorDavis, Larry Sen_US
dc.contributor.authorShet, Vinay Damodaren_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2007-06-22T05:32:11Z
dc.date.available2007-06-22T05:32:11Z
dc.date.issued2007-03-23
dc.description.abstractThe primary objective of an automated visual surveillance system is to observe and understand human behavior and report unusual or potentially dangerous activities/events in a timely manner. Automatically understanding human behavior from visual input, however, is a challenging task. The research presented in this thesis focuses on designing a reasoning framework that can combine, in a principled manner, high level contextual information with low level image processing primitives to interpret visual information. The primary motivation for this work has been to design a reasoning framework that draws heavily upon human like reasoning and reasons explicitly about visual as well as non-visual information to solve classification problems. Humans are adept at performing inference under uncertainty by combining evidence from multiple, noisy and often contradictory sources. This thesis describes a logical reasoning approach in which logical rules encode high level knowledge about the world and logical facts serve as input to the system from real world observations. The reasoning framework supports encoding of multiple rules for the same proposition, representing multiple lines of reasoning and also supports encoding of rules that infer explicit negation and thereby potentially contradictory information. Uncertainties are associated with both the logical rules that guide reasoning as well as with the input facts. This framework has been applied to visual surveillance problems such as human activity recognition, identity maintenance, and human detection. Finally, we have also applied it to the problem of collaborative filtering to predict movie ratings by explicitly reasoning about users preferences.en_US
dc.format.extent1646270 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6715
dc.language.isoen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledArtificial Intelligenceen_US
dc.subject.pquncontrolledBilatticeen_US
dc.subject.pquncontrolledLogical Reasoningen_US
dc.subject.pquncontrolledComputer Visionen_US
dc.subject.pquncontrolledVisual Surveillanceen_US
dc.subject.pquncontrolledCollaborative Filtering;en_US
dc.titleBilattice based Logical Reasoning for Automated Visual Surveillance and other Applicationsen_US
dc.typeDissertationen_US

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