Extracting Places And Activities From GPS Traces Using Hierarchical Conditional Random Fields  

Oleh:

[s.n]

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The International Journal of Robotics Research vol. 26 no. 1 (Jan. 2007), page 119-134.
Topik: activity recognition; conditional random fields; belief propagation; maximum pseudo-likelihood
Fulltext: 119.pdf (393.53KB)

Isi artikel

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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