L. Liao, D. Fox, and H. Kautz.
Extracting Places and
Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research, 2007.
Abstract
Learning patterns of human behavior from sensor data is extremely
important for high-level activity inference. We show how to
extract a person's activities and significant places from traces
of GPS data. Our system uses hierarchically structured
conditional random fields to generate a consistent model of a
person's activities and places. In contrast to existing
techniques, our approach takes high-level context into account in
order to detect the significant places of a person. Our
experiments show significant improvements over existing
techniques. Furthermore, they indicate that our system is able to
robustly estimate a person's activities using a model that is
trained from data collected by other persons.
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This paper is an updated version of our paper published at ISRR-05.
[To the RSE-lab]