Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Joint detection and estimation of multiple objects from image observations
Author: Vo, B.
Vo, B.
Pham, N.
Suter, D.
Citation: IEEE Transactions on Signal Processing, 2010; 58(10):5129-5141
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2010
ISSN: 1053-587X
Statement of
Ba-Ngu Vo, Ba-Tuong Vo, Nam-Trung Pham, and David Suter
Abstract: The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations.
Keywords: Random sets; Multi-Bernoulli; probability hypothesis density (PHD); filtering; images, tracking; track before detect (TBD).
Rights: © 2010 IEEE
RMID: 0020101164
DOI: 10.1109/TSP.2010.2050482
Grant ID:
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.