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Applications of Factorization Theorem and Ontologies for Activity Modeling , Recognition and Anomaly Detection by Umut Akdemir

Title of thesis: Applications of Factorization Theorem and Ontologies for Activity Modeling, Recognition and Anomaly Detection Umut Akdemir, Master of Science, 2005 Thesis directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Affiliate Professor in Department of Computer Science In this thesis two approaches for activity modeling and suspicious activity detection are examined. First is application of factorization theorem extension for deformable models in two different contexts. First is human activity detection from joint position information, and second is suspicious activity detection for tarmac security. It is shown that the first basis vector from factorization theorem is good enough to differentiate activities for human data and to distinguish suspicious activities for tarmac security data. Second approach differentiates individual components of those activities using semantic methodology. Although currently mainly used for improving search and information retrieval, we show that ontologies are applicable to video surveillance. We evaluate the domain ontologies from Challenge Project on Video Event Taxonomy sponsored by ARDA from the perspective of general ontology design principles. We also focused on the effect of the domain on the granularity of the ontology for suspicious activity detection. Applications of Factorization Theorem and Ontologies for Activity Modeling, Recognition and Anomaly Detection