Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

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.

• Type: Journal
• Year: 2007
• Where: IJRR
• Authors: Lin Liao, Dieter Fox, Henry A. Kautz
• Keywords: Conditional Random fields, IJRR 2007, Person’s Activities, Sensor Data