A random finite set conjugate prior and application to multi-target tracking
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The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations and noise. This estimation problem can be formulated in a Bayesian framework by modeling the (hidden) set of states and set of observations as random finite sets (RFSs) where the model for the observation covers thinning, Markov shifts and superposition of false observations. A prior for the hidden RFS together with the likelihood of the realisation of the observed RFS gives the posterior distribution via the application of Bayes rule. We propose a new class of prior distribution and show that it is a conjugate prior with respect to the multi-target observation likelihood. This result is then applied to develop an analytic implementation of the Bayes multi-target filter for the class of linear Gaussian multi-target models. © 2011 IEEE.
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Vo, Ba-Ngu; Vo, Ba Tuong; Phung, D. (2014)An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate ...
Hoseinnezhad, R.; Vo, Ba-Ngu; Vo, Ba Tuong (2013)This correspondence presents a novel method for simultaneous tracking of multiple non-stationary targets in video. Our method operates directly on the video data and does not require any detection. We propose a multi-target ...
Visual tracking of multiple targets by Multi-Bernoulli filtering of background subtracted image dataHoseinnezhad, R.; Vo, Ba-Ngu; Vu, T.N. (2011)Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a visual ...