Incremental Learning For Motion Prediction Of Pedestrians And Vehicles
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Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Author: Alejandro Dizan Vasquez Govea
language: en
Publisher: Springer
Release Date: 2010-07-15
This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.
Incremental Learning for Motion Prediction of Pedestrians and Vehicles
Author: Alejandro Dizan Vasquez Govea
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-23
This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.
Incremental Learning for Motion Prediction of Pedestrians and Vehicles
The main subject of this thesis is motion prediction. The problem is studied on the basis of the assumption that pedestrians and vehicles do not move randomly but follow typical "motion patterns" which may be learned and then user in a prediction phase. The approach addresses three fundamental questions: Modelling: This work is based in the utilisation of a probabilistic model, Hidden Markov Models, to represent typical motion patterns. Learning: This thesis proposes an extension to Hidden Markov Models that allows to learn the structure and parameters of the model in an incremental fashion. Prediction: Prediction is done using exact Bayesian inference. Thanks to the properties of the learned structure, the complexity of inference is linear with respect to the number of states in the model.