TitleLocation-Based Activity Recognition using Relational Markov Networks
Publication TypeConference Paper
Year of Publication2005
AuthorsLiao L, Fox D, Kautz H
Conference NameIJCAI

<p>In this paper we define a general framework for activity recognition by building upon and extending Relational Markov Networks. Using the example of activity recognition from location data, we show that our model can represent a variety of features including temporal information such as time of day, spatial information extracted from geographic databases, and global constraints such as the number of homes or workplaces of a person. We develop an efficient inference and learning technique based on MCMC. Using GPS location data collected by multiple people we show that the technique can accurately label a person's activity locations. Furthermore, we show that it is possible to learn good models from less data by using priors extracted from other people's data.</p>


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Citation KeyLia05Loc