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Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even for non-graphic related applications. The field of nonlinear state estimation is no exception here. Particle Filters have already been successfully ported to GPUs. In this paper, we propose a GPU-accelerated variant of the Progressive Gaussian Filter (PGF). This allows us to combine the advantages...
When tracking an extended object, traditional approaches exploit information only from measurements that are assumed to stem from the target, and discard observations assumed to have been generated elsewhere. However, the fact that these observations were received contains valuable information about where the target is not. This information, which is usually treated as clutter with little value, can...
As sensor resolution increases, the accuracy and robustness of tracking algorithms can be improved by incorporating more information about the shape of the target object. This raises the need for simple and robust shape models capable of describing detailed objects. In this paper we propose an approach based on Random Hypersurface Models that interprets target shapes as scaled extrusions. This is...
As sensor resolution increases, estimators tracking extended objects benefit from being able to closely model the shape of the target. However, as more shape details are incorporated, this usually leads to increasingly complex estimators. A more useful approach is to describe these shapes as a combination of simpler shapes connected to each other. In this paper, we propose a modular approach to estimate...
In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular Nonlinear Kalman Filters, e.g., the UKF, we do not rely on linearizing the measurement model. Instead, we take up the Gaussian progressive filtering approach introduced by the PGF 42 but explicitly rely on likelihood functions. Progression means, we incorporate the information of a new measurement gradually...
In this paper, we consider the problem of jointly tracking the pose and shape of objects based on noisy data from cameras and depth sensors. Our proposed approach formalizes object silhouettes from image data as measurements within a Bayesian estimation framework. Projecting object silhouettes from images back into space yields a visual hull that constrains the object. In this work, we focus on the...
An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart Sampling Kalman Filter (S2KF) is introduced. It is based on a new low-discrepancy Dirac Mixture approximation of Gaussian densities. The approximation comprises an arbitrary number of optimally and deterministically placed samples in the entire state space, so that the filter resolution can be adapted to either achieve...
In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is derived. Each given measurement stems from an unknown point and is corrupted with additive Gaussian noise. Extreme value theory is applied in order to derive a linear measurement equation for the problem. The new estimator is applied to the problem of group target...
Target tracking algorithms usually assume that the received measurements stem from a point source. However, in many scenarios this assumption is not feasible so that measurements may stem from different locations, named measurement sources, on the target surface. Then, it is necessary to incorporate the target extent into the estimation procedure in order to obtain robust and precise estimation results...
In this paper, a novel approach for tracking extended objects is presented. The target object is modeled as a circular disc such that the center and extent of the target object can be estimated. At each time step, a finite set of position measurements that are corrupted with stochastic noise may be available. Each position measurement stems from an unknown measurement source on the extended object...
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