| Title: |
Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking |
| Authors: |
Mihaylova, Lyudmila; Carmi, Avishy; Septier, François; Gning, Amadou; Pang, Sze Kim; Godsill, Simon J. |
| Contributors: |
Department of Automatic Control and Systems Engineering; Automatic Control and Systems Engineering; Department of Mechanical Engineering Beer-Sheva; Ben-Gurion University of the Negev (BGU); LAGIS-SI; Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS); Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS); Institut TELECOM/TELECOM Lille1; Institut Mines-Télécom Paris (IMT); Department of Computer Science; University College of London London (UCL); DSO National Laboratories; Signal Processing Group Dept of Engineering; University of Cambridge UK (CAM) |
| Source: |
ISSN: 1051-2004. |
| Publisher Information: |
HAL CCSD; Elsevier |
| Publication Year: |
2014 |
| Subject Terms: |
Sequential Monte Carlo methods; Group and extended object tracking; Markov chain Monte Carlo methods; Nonlinear filtering; Metropolis Hastings; Reasoning over time; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://hal-imt.archives-ouvertes.fr/hal-00937289 |
| Availability: |
https://hal-imt.archives-ouvertes.fr/hal-00937289 |
| Rights: |
undefined |
| Accession Number: |
edsbas.CE8B8F1C |
| Database: |
BASE |