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Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

Mihaylova, Lyudmila; Carmi, Avishy Y.; Septier, François; Gning, Amadou; Pang, Sze Kim; Godsill, Simon


Lyudmila Mihaylova

Avishy Y. Carmi

François Septier

Amadou Gning

Sze Kim Pang

Simon Godsill


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.


Mihaylova, L., Carmi, A. Y., Septier, F., Gning, A., Pang, S. K., & Godsill, S. (2014). Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking. Digital Signal Processing, 25, 1-16.

Journal Article Type Article
Online Publication Date Dec 4, 2013
Publication Date 2014-02
Deposit Date Jan 2, 2019
Publicly Available Date Jan 3, 2019
Journal Digital Signal Processing
Print ISSN 1051-2004
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 25
Pages 1-16
Keywords Signal Processing; Electrical and Electronic Engineering
Public URL
Publisher URL


Article (1.7 Mb)

Copyright Statement
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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