[Legacy Report] A theoretical framefork for activity classification

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Shape analysis is playing an increasingly important role in many
applications where object classi cation and understanding are of
interest. Solutions to many existing as well as new emerging applied
problems (e.g, object recognition, biometrics etc.) crucially depend
on object modeling and their parsimonious representation.
Modeling an active silhouette in a video sequence provides a good
solution for activity surveillance. We pose this problem as one of
tracking a flow of shapes as entities on a curved space. We rst
propose a stochastic model for a flow on a manifold to carry out
classification of different processes. We then exploit this insight to
develop a tracking filter of these shapes and subsequently propose a
generative model useful in a variety of applications. We subsequently
propose a generative model for human activity. We provide
substantiating illustrations.

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