AI Seminar
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  Tuesday, January 25th, 2005
  4:00 pm - 5:30 pm
  175 ATL (Large Conference Room)

  "Location Estimation for Activity Recognition"
  Dieter Fox
  Computer Science and Engineering
  University of Washington, Seattle

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Knowledge of a person's location provides important context
information for many pervasive computing applications.  Beyond this,
location information is extremely helpful for estimating a person's
high-level activities. In this talk we show how Bayesian filtering can
be applied to estimate the location of a person using sensors such as
GPS, infrared, or WiFi.  The techniques track a person on graph
structures that represent a street map or a skeleton of the free space
in a building.  In the context of GPS, we show how such a graph
representation can be embedded into a hierarchical activity model that
learns and infers a user's daily movements through the community.  The
model uses multiple levels of abstraction in order to bridge the gap
between raw GPS measurements and high level information such as a
user's mode of transportation or her goal. We also briefly discuss
very recent work on RFID-based activity recognition and relational
Markov networks for place labeling.


Bio:
Dieter Fox is an Assistant Professor of Computer Science & Engineering
at the University of Washington, Seattle. He obtained his Ph.D. from
the University of Bonn, Germany.  Before joining UW, he spent two
years as a postdoctoral researcher at the CMU Robot Learning Lab. His
research focuses on probabilistic state estimation in robotics and
activity recognition.  He received various awards, including an NSF
CAREER award and best paper awards at major robotics and artificial
intelligence conferences.