Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Learning and inferring transportation routines
Artificial Intelligence
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Modeling time-varying uncertain situations using Dynamic Influence Nets
International Journal of Approximate Reasoning
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
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Recognizing plans of moving agents is a natural goal for many sensor systems, with applications including robotic pathfinding, traffic control, and detection of anomalous behavior. This paper considers plan recognition complicated by the absence of contextual information such as labeled plans and relevant locations. Instead, we introduce 2 unsupervised methods to simultaneously estimate model parameters and hidden values within a Factor graph representing agent transitions over time. We evaluate our approach by applying it to goal prediction in a GPS dataset tracking 1074 ships over 5 days in the English channel.