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

被引：230

作者：

Liao, Lin ^{[1
]}

Fox, Dieter ^{[1
]}

Kautz, Henry ^{[1
]}

机构：

[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA

来源：

INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH | 2007年 / 26卷 / 01期

关键词：

activity recognition; conditional random fields; belief propagation; maximum pseudo-likelihood;

D O I：

10.1177/0278364907073775

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

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.

引用

页码：119 / 134

页数：16