Tuesday, April 29, 2008
4:00 PM
Computer Studies Bldg. Room 703
Ph.D. Thesis Proposal
Ross Messing
University of Rochester
Activity Recognition using Augmented Feature Flow
Activity recognition systems have made extensive use of local motion,
but have largely failed to use long-range motion information. We
develop a new way to represent the motion information in a video
sequence that captures long-range motion of features. We show that
this information can be used to effectively disambiguate activities,
particularly when it is augmented by information orthogonal to motion,
like position. We propose a number of ways to extend this work. We
will investigate new representations of feature motion, and how to
tractably characterize the relationship between a pair of moving
features. We will also explore powerful ways to augment the motion
information with much stronger position information (relative location
of features to a visual landmark) and appearance information (object
recognition on image patches extracted from the feature). Lastly, we
propose a number of ways to make our model more robust and
nonparametric.