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dc.contributor.authorHamid, Muhammad Raffayen_US
dc.date.accessioned2008-09-17T19:35:47Z
dc.date.available2008-09-17T19:35:47Z
dc.date.issued2008-07-07en_US
dc.identifier.urihttp://hdl.handle.net/1853/24765
dc.description.abstractIn order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of everyday human activities. For a majority of environments, the structure of the in situ activities is generally not known a priori. This thesis therefore investigates knowledge representations and manipulation techniques that can facilitate learning of such everyday human activities in a minimally supervised manner. A key step towards this end is finding appropriate representations for human activities. We posit that if we chose to describe activities as finite sequences of an appropriate set of events, then the global structure of these activities can be uniquely encoded using their local event sub-sequences. With this perspective at hand, we particularly investigate representations that characterize activities in terms of their fixed and variable length event subsequences. We comparatively analyze these representations in terms of their representational scope, feature cardinality and noise sensitivity. Exploiting such representations, we propose a computational framework to discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding concise characterizations of these discovered activity-classes, both from a holistic as well as a by-parts perspective. Using such characterizations, we present an incremental method to classify a new activity instance to one of the discovered activity-classes, and to automatically detect if it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our framework in a variety of everyday environments.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectComputational perceptionen_US
dc.subjectActivity recognitionen_US
dc.subjectAnomaly detectionen_US
dc.subjectArtificial intelligenceen_US
dc.subjectBehavior modelingen_US
dc.subjectScene understandingen_US
dc.subject.lcshAutomatic data collection systems
dc.subject.lcshUbiquitous computing
dc.subject.lcshOptical detectors
dc.subject.lcshHuman-computer interaction
dc.subject.lcshExpert systems (Computer science)
dc.subject.lcshComputer vision
dc.titleA computational framework for unsupervised analysis of everyday human activitiesen_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentComputingen_US
dc.description.advisorCommittee Chair: Aaron Bobick; Committee Member: Charles Isbell; Committee Member: David Hogg; Committee Member: Irfan Essa; Committee Member: James Rehgen_US


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