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| Volume 26 Issue 1 - Publication Date: 1 January 2007 |
| Special Issue: The 12th International Symposium on Robotics Research |
| Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields |
| L. Liao, D. Fox and H. Kautz Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195 |
| 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. |
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