Hierarchical Conditional Random Fields for GPS-based Activity Recognition
Submitted by mkrainin on Tue, 2012-02-28 12:21
| Title | Hierarchical Conditional Random Fields for GPS-based Activity Recognition |
| Publication Type | Book Chapter |
| Year of Publication | 2007 |
| Authors | Liao L, Fox D, Kautz H |
| Book Title | Robotics Research – Results of the 12th International Symposium ISRR |
| 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 locations 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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| Citation Key | Lia07Hie |
Last changed Tue, 2012-03-20 17:59

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