{"type":"ScholarlyArticle","title":"Location-Based Activity Recognition using Relational Markov Networks","authors":[{"name":"Lin Liao","slug":"lin-liao","given":"Lin","family":"Liao","affiliation":""},{"name":"Dieter Fox","slug":"dieter-fox","given":"Dieter","family":"Fox","affiliation":""},{"name":"Henry A. Kautz","slug":"henry-a-kautz","given":"Henry A.","family":"Kautz","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2005/212","pdf":"https://ijcai.org/proceedings/2005/0212.pdf","markdown":"https://ijcai.org/proceedings/2005/212.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/212","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":212,"pages":{"first":773,"last":777},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}