L. Liao, D. Fox, and H. Kautz.
Learning and Inferring
Outstanding Paper Award
Proc. of the National Conference on
Artificial Intelligence (AAAI-04).
Abstract This paper introduces a hierarchical Markov model that
can learn and infer a user's daily movements through the
community. The model uses multiple levels of abstraction in
order to bridge the gap between raw GPS sensor measurements
and high level information such as a user's mode of
transportation or her goal. We apply Rao-Blackwellised
particle filters for efficient inference both at the low level
and at the higher levels of the hierarchy. Significant
locations such as goals or locations where the user frequently
changes mode of transportation are learned from GPS data logs
without requiring any manual labeling. We show how to detect
abnormal behaviors (\eg\ taking a wrong bus) by concurrently
tracking his activities with a trained and a prior model.
Experiments show that our model is able to accurately predict
the goals of a person and to recognize situations in which the
user performs unknown activities.
Full paper [pdf]
(144 kb), 6 pages.
[To the RSE-lab]