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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TLDR
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
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Learning Stochastic Path Planning Models from Video Images
Sridevi Parise,Padhraic Smyth +1 more
TL;DR: A probabilistic framework for learning models of pedestrian trajectories in general outdoor scenes based on a combination of Kalman filters and stochastic path-planning via landmarks, where the landmarks are learned from the data.
Journal ArticleDOI
Mobility Prediction using Modified RBF Network
Mohammad F. Hassanin,Amr Badr +1 more
TL;DR: Modified Radial Basis Function Networks (RBF Network) algorithm is developed to predict user’s locations using clustering data using DBCLUM then using backpropagation algorithm for classifying data in on time using RBF Network.
Lightweight Semantic Location and Activity Recognition on Android Smartphones with TensorFlow
TL;DR: A study on sensor-based, low-power Semantic Location and Activity Recognition for Android devices using Keras and TensorFlow frameworks.
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.