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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Hierarchical Conditional Random Fields for GPS-Based Activity Recognition
verfasst von : Lin Liao, Dieter Fox, Henry Kautz
Erschienen in: Robotics Research
Verlag: Springer Berlin Heidelberg
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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.