Location-Based Activity Recognition using Relational Markov Networks
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.
@inproceedings{ijcai2005p212,
title = {Location-Based Activity Recognition using Relational Markov Networks},
author = {Liao, Lin and Fox, Dieter and Kautz, Henry A.},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {773--777},
year = {2005},
url = {https://ijcai.org/proceedings/2005/212},
}
TY - CPAPER TI - Location-Based Activity Recognition using Relational Markov Networks AU - Liao, Lin AU - Fox, Dieter AU - Kautz, Henry A. T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 773 EP - 777 UR - https://ijcai.org/proceedings/2005/212 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
