Extracting places and activities from gps traces using hierarchical conditional random fields

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Extracting places and activities from gps traces using hierarchical conditional random fields (2007)

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by Lin Liao , Dieter Fox , Henry Kautz

Venue:	International Journal of Robotics Research
Citations:	119 - 3 self

BibTeX

@ARTICLE{Liao07extractingplaces,
    author = {Lin Liao and Dieter Fox and Henry Kautz},
    title = {Extracting places and activities from gps traces using hierarchical conditional random fields},
    journal = {International Journal of Robotics Research},
    year = {2007},
    volume = {26},
    pages = {2007}
}

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Abstract

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. 1

Keyphrases

gps trace hierarchical conditional random field person activity significant place human behavior high-level context gps data high-level activity inference sensor data significant improvement consistent model structured conditional random field

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