Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
نویسندگان
چکیده
منابع مشابه
Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
a University of Sheffield, Department of Automatic Control and Systems Engineering, Mappin Street, Sheffield S1 3JD, United Kingdom b Ben-Gurion University of the Negev, Department of Mechanical Engineering, Beer-Sheba 8410501, Israel c Institut Mines-Télécom / Télécom Lille 1 / LAGIS UMR CNRS 8219, Cité Scientifique, Rue Guglielmo Marconi, BP 20145, 59653 Villeneuve d’Ascq Cedex, France d Univ...
متن کاملA Sequential Monte Carlo Framework for Extended Object Tracking
In this paper we consider the problem of extended object tracking. An extended object is modelled as a set of point features in a target reference frame. The dynamics of the extended object are formulated in terms of the translation and rotation of the target reference frame relative to a fixed reference frame. This leads to realistic, yet simple, models for the object motion. We assume that th...
متن کاملSequential Monte Carlo methods for Bayesian object matching
In dynamic state-space problems, Sequential Monte Carlo (SMC) methods are familiar techniques for obtaining samples from the Bayesian posterior distribution and updating the sample set as new observations arrive. The methods can also be applied in static problems by exposing the observations gradually. In our static object matching problem, we adopt a different approach; all the data is availab...
متن کاملSequential Monte Carlo Methods for Multi-Object Tracking
This document provides an overview over literature relevant to (multi-) object tracking based on sequential Monte Carlo methods. Besides milestones like [IB98a] (CONDENSATION) or [DdFG02] (sequential Monte Carlo methods), there are also some less fundamental articles, presenting some original ideas or extend the basic algorithms in a remarkable way. The reviewed articles are grouped in two majo...
متن کاملA sequential Monte Carlo approach for extended object tracking in the presence of clutter
Extended objects are characterised with multiple measurements originated from different locations of the object surface. This paper presents a novel Sequential Monte Carlo (SMC) approach for extended object tracking in the presence of clutter. This framework is formulated for general nonlinear problems. The main contribution of this work is in the derivation of the likelihood function for nonli...
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: Digital Signal Processing
سال: 2014
ISSN: 1051-2004
DOI: 10.1016/j.dsp.2013.11.006