Name
Abstract | ||
|---|---|---|
This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through an urban community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's destination and mode of transportation. To achieve efficient inference, we apply Rao-Blackwellized particle filters at multiple levels of the model hierarchy. Locations such as bus stops and parking lots, where the user frequently changes mode of transportation, are learned from GPS data logs without manual labeling of training data. We experimentally demonstrate how to accurately detect novel behavior or user errors (e.g. taking a wrong bus) by explicitly modeling activities in the context of the user's historical data. Finally, we discuss an application called ''Opportunity Knocks'' that employs our techniques to help cognitively-impaired people use public transportation safely. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.artint.2007.01.006 | Artificial Intelligence |
Keywords | DocType | Volume |
higher level,hierarchical markov model,novelty detection,low level,high level information,raw gps sensor measurement,abnormal behavior,gps data log,prior model,activity recognition,bus stop,rao-blackwellised particle filter,training data,rao-blackwellized particle filters,historical data,public transportation,inferring transportation routine,location tracking,multiple level,user error,model hierarchy,markov model | Conference | 171 |
Issue | ISSN | ISBN |
5-6 | 0004-3702 | 0-262-51183-5 |
Citations | PageRank | References |
396 | 43.17 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Liao | 1 | 1649 | 209.71 |
| Donald J. Patterson | 2 | 1765 | 219.99 |
| Dieter Fox | 3 | 12306 | 1289.74 |
| Henry A. Kautz | 4 | 9271 | 1010.27 |