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2007
Springer

Location-Based Activity Recognition

10 years 7 months ago
Location-Based Activity Recognition
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the highlevel context into account. Our system uses relational Markov networks to represent the hierarchical activity model that encodes the complex relations among GPS readings, activities and significant places. We apply FFT-based message passing to perform efficient summation over large numbers of nodes in the networks. We present experiments that show significant improvements over existing techniques.
Dieter Fox
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where KI
Authors Dieter Fox
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