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

dierent 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 dierentiate activities for human data and to distinguish

suspicious activities for tarmac security data.

Second approach dierentiates individual components of those activities using semantic methodol-

ogy. 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 eect of the domain on the granularity of the ontology

for suspicious activity detection.