Joint detection and estimation of multiple objects from image observations

Authors:
Ba-Ngu Vo;Ba-Tuong Vo;Nam-Trung Pham;David Suter
Affiliations:
School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA;School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, WA;Institute for Infocomm Research, Singapore;School of Computer Science, University of Adelaide, North Terrace
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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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.