The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a ...

The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates the intensity function and the posterior cardinality distribution. While there are a few new approaches to enhance the Sequential Monte Carlo (S...

Abelian group, 278 acceptance probability, 24, 37, 246 adaptive Metropolis algorithm, 33 Akaike’s information criterion, 209, 320 alpha-beta recursion, 11, 390 annealed importance sampling, 321 aperiodic chain, 23 APF, see auxiliary particle filter AR model, see autoregressive model assumed density filtering, 17, 143, 388 autoregressive hidden Markov model, 185 autoregressive model, 4, 9, 111 a...

We apply the auxiliary particle filter algorithm of Pitt and Shephard (1999) to the problem of robot localization. To deal with the high-dimensional sensor observations (images) and an unknown observation model, we propose the use of an inverted nonparametric observation model computed by nearest neighbor conditional density estimation. We show that the proposed model can lead to a fully adapte...

We present in this paper a sequential Monte Carlo methodology for joint detection and tracking of a multiaspect target in image sequences. Unlike the traditional contact/association approach found in the literature, the proposed methodology enables integrated, multiframe target detection and tracking incorporating the statistical models for target aspect, target motion, and background clutter. ...

In this paper we are concerned with nonlinear systems subject to a conditionally linear, Gaussian sub-structure. This structure is often exploited in high-dimensional state estimation problems using the marginalized (aka Rao-Blackwellized) particle lter. The main contribution in the present work is to show how an e cient lter can be derived by exploiting this structure within the auxiliary part...

In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effect. Our idea relies on the auxiliary particle filter method that allows to sequentially evaluate the parameters and the latent processes involved in the dynamic. An empirical application on simulated data is presented to study some empirical properties of the algorithm impleme...