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IJRR
2007

Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

9 years 11 months ago
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
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.
Lin Liao, Dieter Fox, Henry A. Kautz
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where IJRR
Authors Lin Liao, Dieter Fox, Henry A. Kautz
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