Location-Based Activity Recognition

May 17, 2006
Dieter Fox | University of Washington

Knowledge of a person’s location provides important context information for many applications, ranging from services such as E911 to personal guidance systems that help cognitively-impaired individuals move safely through their community. Location information is also extremely helpful for estimating a person’s high-level activities. In this talk we show how Bayesian filtering and conditional random fields can be applied to estimate the location and activity of a person using sensors such as GPS 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. We also show how to learn a user’s significant places and daily movements through the community. Our models use multiple levels of abstraction so as to bridge the gap between raw GPS measurements and high level information such as a user’s mode of transportation, her current goal, and her significant places (e.g. home or work place). Finally, we will discuss recent work on using a multi-sensor board so as to better estimate a person’s activities.

Speaker Details

Dieter Fox is Associate Professor and Director of the Robotics and State Estimation Lab in the Computer Science & Engineering Department 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. Along with his colleagues, he introduced particle filters as a powerful tool for state estimation in robotics. More recently, he showed how to use hierarchical dynamic Bayesian networks and relational statistical learning techniques in order to extract high-level activity information from raw sensor data. Fox received various awards for his research, including an NSF CAREER award and best paper awards at major robotics (IROS-98, ICRA-00) and artificial intelligence conferences (AAAI-98, AAAI-04).