Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets

Particle filter for extracting target label information when targets move in close proximity

This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in close proximity. Having this approximation available, we are able to extract information about target labels even when the measurements do not provide information about target's identities. In addition, due to the structure of our particle filter, we are able to use an estimator that provides lower optimal subpattern assignment (OSPA) errors than usual estimators

Nonlinear filtering update phase via the Single Point Truncated Unscented Kalman filter

A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kalman filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter

Gate Volume Estimation for Target Tracking

Gate volume is an integral part of many algorithms for non-parametric target tracking in clutter. Non-parametric target tracking assumes no a-priori knowledge of clutter measurement density. In the simplest case of a single-target single-state target tracking filter employing a Gaussian approximation, the gate is a hyper-ellipsoid whose volume can be calculated analytically. However, in a multi-target environment with closely spaced targets or in single target tracking with a mixture Gaussian approximation, the gate consists of overlapping hyper-ellipsoids and analytical evaluation of the gate volume is not possible. This paper presents a general algorithm for approximate estimation of volume of overlapping gates, together with tradeoffs between computing resources, volume estimation errors and its effects on target tracking performance

Multiple Target Tracking Based on Sets of Trajectories

We propose a solution of the multiple target tracking (MTT) problem based on sets of trajectories and the random finite set framework. A full Bayesian approach to TT should characterize the distribution of the trajectories given the measurements, as it contains all information about the trajectories. We attain this by considering multiobject density functions in which objects are trajectories. For the standard tracking models, we also describe a conjugate family of multitrajectory density functions

Estimating The dynamics of aberration components in the human eye

To provide adequate information that would assist surgeons in performing advanced refractive corrections, it is now essestial to address the problem of microfluctuations in the eye's aberrations due to pulse and respiration. Although the effects of fluctuations in defocus are known and well described, very little is reported on modelling the fluctuations in other types of aberrations. We propose a methodology in which the dynamics of higher order aberation components are modelled by parametric AM-FM signals. Using our modelling approach, the effects of changes in these aberrations could be predicted and studied. In particular, we model the dynamics of components related to coma and sperical aberration. We provide a validation of the proposed modelling approach using aberration data from the eyes of six subjects

Fundamentals of Object Tracking

Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples