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|Title:||Joint detection and estimation of multiple objects from image observations|
|Citation:||IEEE Transactions on Signal Processing, 2010; 58(10):5129-5141|
|Publisher:||IEEE-Inst Electrical Electronics Engineers Inc|
|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|
|Appears in Collections:||Computer Science publications|
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