Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
(CS 528)
Location Estimation for Activity Recognition
Dieter Fox, University of Washington
September 27, 2004, 4:15PM
TCSeq 200
http://graphics.stanford.edu/ba-colloquium/
Abstract
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 technique tracks a person on a graph
structure that represents a streetmap or a skeleton of the free space
in a building. We show how such a graph representation can be
embedded into a hiearchical 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. Finally, we present early work on
estimating a person's outdoor location from WiFi access points. The
technology uses GPS-annotated connectivity traces to learn a sensor
model suited for location estimation.
About the Speaker
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 receieved various awards, including an NSF
CAREER award and best paper awards at major robotics and artificial
intelligence conferences.
Contact: bac-coordinators@cs.stanford.edu
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