Bayesian Sensor Fusion Methods for Dynamic Object Tracking—A Comparative Study
نویسندگان
چکیده
منابع مشابه
Bayesian Sensor Fusion Methods for Dynamic Object Tracking — A Comparative Study
In this paper we study the problem of Bayesian sensor fusion for dynamic object tracking. The prospects of utilizing measurements from several sensors to infer about a system state are manyfold and they range from increased estimate accuracy to more reliable and robust estimates. Sensor measurements may be combined, or fused, at a variety of levels; from the raw data level to a state vector lev...
متن کاملBayesian Sensor Fusion for Cooperative Object Localization and World Modeling
This paper introduces a method for representing, communicating and fusing distributed, noisy and partial observations of an object by multiple robots. This technique describes how to model sensors and the information they acquire. Each sensor is considered as a team member making decisions locally to achieve a local estimate. The local estimates of a robot are then fused with the other robots l...
متن کاملBayesian Decision Fusion for Dynamic Multi-Cue Object Detection
Visual object detection using single cue information has been successfully applied in various tasks, in particular for near range recognition. While robust classification and probabilistic representation enhance 2D pattern recognition performance, they are ’per se’ restricted due to the limited information content of single cues. The contribution of this work is to demonstrate performance impro...
متن کاملDistributed Sensor Fusion for Object Tracking
In a dynamic situation like robot soccer any individual player can only observe a limited portion of their environment at any given time. As such to develop strategies based upon planning and cooperation between different players it is imperative that they be able to share information which may or may not be in any individual player’s field of vision. In this paper we propose a method for multi...
متن کاملComparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian infere...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Automatika
سال: 2014
ISSN: 0005-1144,1848-3380
DOI: 10.7305/automatika.2014.09.847