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We propose a new algorithm, called the central difference filter - Kalman filter (CDF-KF) for conditionally linear Gaussian state space models. The linear state equation is firstly inserted into the measurement equation, and the CDF is applied to the new measurement and the nonlinear state equations to estimate the nonlinear states, where after the estimated means of the nonlinear states are substituted...
We firstly constructed a new dynamic state space model with little exact knowledge of the original state dynamics by using the polynomial predictive filter and state dimension extension. Then a particle filter was used to estimate the extended state, where the sum of the extended particle weights was applied to test whether the filter is convergent or not. Finally the estimate of the original state...
A new multi-target tracking algorithm, termed as the Gaussian sum convolution probability hypothesis density (GSCPHD) filter, is proposed. The filter is calculated by a bank of convolution filters with Gaussian approximations to the predicted and posterior densities. It is shown that the ability to deal with complex observation model, non or small observation noise of the GSCPHD over the Gaussian...
We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context,...
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