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Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
Authors
Abdallah
Ali
+150 more
Amadou Gning
Andrieu
Angelova
Avishy Y. Carmi
Bar-Shalom
Bardet
Baum
Baum
Baum
Baum
Berzuini
Blaser
Bocquel
Bocquel
Bocquel
Boers
Boers
Bolic
Borkar
Brasnett
Carmi
Carmi
Carmi
Celikkanat
Chen
Clark
Clark
Coates
Cristiani
Culham
Daley
Davenport
Davey
Del Moral
Delgado-Gonzalo
Djuric
Djurić
Doucet
Doucet
Feldmann
Feldmann
Fox
François Septier
Gilholm
Gilholm
Gilks
Gning
Gning
Gning
Gordon
Grandström
Granström
Granström
Granström
Granström
Granström
Gustafsson
Helbing
Helbing
Hendeby
Henriksen
Hue
Hughes
Ing
Jaward
Julier
Khan
Khan
Khan
Koch
Koch
Koch
Kreucher
Laet
Lan
Lan
Lee
Lerner
Lian
Liu
Lundgren
Lundquist
Lyudmila Mihaylova
Mahler
Mahler
Mahler
Mahler
Mahler
Mahler
Mahler
Mahler
Mahler
Manfredotti
Maskell
Mazzon
Mehran
Mihaylova
Mihaylova
Murphy
Míguez
Nakano
Olivieri
Pang
Pang
Pellegrini
Petrov
Reynolds
Ristic
Ristic
Ristic
Ristic
Ristic
Robert
Salmond
Salmond
Schikora
Schikora
Schikora
Schön
Schön
Septier
Septier
Shen
Sheng
Simon Godsill
Stoyan
Streit
Streit
Sun
Sutharsan
Swain
Swain
Sze Kim Pang
Särkkä
van Leeuwen
Vermaak
Vermaak
Vo
Vo
Vo
Vo
Vo
Vo
Vo
Waxman
Wen
Wieneke
Wieneke
Xing
Zhan
Publication date
4 December 2013
Publisher
'Elsevier BV'
Doi
Cite
Abstract
This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced. © 2013 Elsevier Inc. All rights reserved
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