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                   B O S T O N   U N I V E R S I T Y
                      Computer Science Department
                          C O L L O Q U I U M
                      Wednesday, March 27, 11:00 AM 
                       (Coffee served at 10:45 AM)
                         Seminar Room / MCS 135


 Switching Linear Dynamic Models: Learning, Tracking, and Synthesis of
                              Human Motion



                           Vladimir Pavlovic
                           Boston University


                              Abstract


The human figure exhibits complex and rich dynamic behavior that is both
nonlinear and time-varying.  However, most work on tracking and analysis
of figure motion has employed either generic or highly specific
hand-tailored dynamic models superficially coupled with models of motion
regimes. I will present an alternative class of learned dynamic models
known as switching linear dynamic systems (SLDSs).  Cast in the
framework of dynamic Bayesian networks, SLDS can be applied to analysis,
tracking, and synthesis of the human figure motion.  Since exact
inference in SLDS is intractable, I will introduce two novel approximate
inference algorithms based on structured variational and
winner-takes-all approximations.  Experimental results on learning,
analysis, and synthesis of figure dynamics from video data indicate
significant advantages and potential of the SLDS approach.

Bio:

Vladimir Pavlovic is a research assistant professor in the Bioinformatics 
Program at Boston University. Before joining Boston University, Vladimir was a
member of research staff at Compaq's Cambridge Research Laboratory (CRL)
in Cambridge, MA. His interests include applications of machine 
learning and probabilistic inference to problems in computer vision, HCI,
bioinformatics, and modeling of complex systems. Vladimir received a
Ph.D. in electrical and computer engineering from the University of
Illinois at Urbana-Champaign in 1999.


Host: Stan Sclaroff
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