Extracting places and activities from GPS traces using hierarchical conditional random fields
Extracting places and activities from GPS traces using hierarchical conditional random fields
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DOI：
10.1177/0278364907073775
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发表时间：
2007-01-01
影响因子：
9.2
通讯作者：
Kautz, Henry
中科院分区：
文献类型：
--
作者：
Liao, Lin;Fox, Dieter;Kautz, Henry
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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.