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


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.


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