Book ChapterDOI
Inferring High-Level Behavior from Low-Level Sensors
Donald J. Patterson,Lin Liao,Dieter Fox,Henry Kautz +3 more
- pp 73-89
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In this paper, a method of learning a Bayesian model of a traveler moving through an urban environment is presented, which simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner.Abstract:
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.read more
Citations
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Proceedings Article
Behavior recognition in assisted cognition
TL;DR: In principle, this paper shall take a decision-theoretic perspective and compute the expected costs and benefits under uncertainty about the world and user state at hand and discuss future research directions.
Journal ArticleDOI
Agatha: predicting daily activities from place visit history for activity-aware mobile services in smart cities
TL;DR: A place-history-based activity prediction system called Agatha, in order to enable activity-aware mobile services in smart cities and evaluates the prediction model using the American Time-Use Survey (ATUS) dataset, which includes more than 10,000 people's location and activity history.
Journal ArticleDOI
Group Behavior Recognition for Gesture Analysis
TL;DR: The movements of the human body limbs and center of gravity are analyzed in order to detect and analyze simple actions such as walking and running and a novel framework for online probabilistic plan recognition in cooperative multiagent systems is proposed: the Multiagent Hidden Markov mEmory Model (M-AHMEM), which is a dynamic Bayesian network.
Book ChapterDOI
Hierarchical Goal Recognition
Nate Blaylock,James F. Allen +1 more
TL;DR: This chapter discusses hierarchical goal recognition: simultaneous online recognition of goals and subgoals at various levels within an HTN-like plan tree using statistical, graphical models to recognize hierarchical goal schemas in time quadratic with the number of the possible goals.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Estimation with Applications to Tracking and Navigation
TL;DR: Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations using a balanced combination of linear systems, probability, and statistics.
Journal ArticleDOI
Location systems for ubiquitous computing
TL;DR: This survey and taxonomy of location systems for mobile-computing applications describes a spectrum of current products and explores the latest in the field to help developers of location-aware applications better evaluate their options when choosing a location-sensing system.
Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.