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The International Journal of Robotics Research
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Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

Lin Liao

Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, liaolin{at}cs.washington.edu

Dieter Fox

Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, fox{at}cs.washington.edu

Henry Kautz

Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, kautz{at}cs.washington.edu

Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person’s activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In contrast to existing techniques, this approach takes the high-level context into account in order to detect the significant places of a person. Experiments show significant improvements over existing techniques. Furthermore, they indicate that the proposed system is able to robustly estimate a person’s activities using a model that is trained from data collected by other persons.

Key Words: activity recognition • conditional random fields • belief propagation • maximum pseudo-likelihood

The International Journal of Robotics Research, Vol. 26, No. 1, 119-134 (2007)
DOI: 10.1177/0278364907073775
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