---
title: "Location-Based Activity Recognition using Relational Markov Networks"
authors:
  - "Lin Liao"
  - "Dieter Fox"
  - "Henry A. Kautz"
doi: ""
url: "https://ijcai.org/proceedings/2005/212"
pdf: "https://ijcai.org/proceedings/2005/0212.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "773-777"
track: ""
keywords:
---

# Location-Based Activity Recognition using Relational Markov Networks

Lin Liao, Dieter Fox, Henry A. Kautz

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 773-777.

## Abstract

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.