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

TitleExtracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
Publication TypeJournal Article
Year of Publication2007
AuthorsLiao L, Fox D, Kautz H
JournalInternational Journal of Robotics Research
Volume26
Abstract<p>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.</p>
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Last changed Tue, 2012-03-20 17:57