Bayesian multi-object estimation from image observations
Ba‐Ngu Vo, Ba-Tuong Vo, Nam Trung Pham, David Suter · UWA Profiles and Research Repository (UWA) · 2009
Analytic characterizations of the posterior distribution of a random finite set of states, conditioned on image observations are derived; under the assumption that the regions of the observation influenced by individual states do not overlap. These results provide tractable means to jointly estimate the number of states and their values in the Bayesian framework. As an application, we develop a multiobject filter suitable for image observations with low signal to noise ratio. A particle implementation of the multi-object filter is proposed and demonstrated via simulations.