Lab Meeting 1 October (Yu-Hsiang):Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

International Journal of Robotics Research, 2007

Author : Lin Liao

Abstract:
Learning patterns of human behavior from sensor data is extremely important for high-levelactivity inference. We show how to extract a person’s activities and significant places from tracesof GPS data. Our system uses hierarchically structured conditional random fields to generate aconsistent model of a person’s activities and places. In contrast to existing techniques, our approachtakes 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 indicatethat our system is able to robustly estimate a person’s activities using a model that is trained fromdata collected by other persons.

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