11 research outputs found
A Novel Bayesian Method for Fitting a Circle to Noisy Points
This paper introduces a novel recursive Bayesian estimator for the center and radius of a circle based on noisy points. Each given point is assumed to be a noisy measurement of an unknown true point on the circle that is corrupted with known isotropic Gaussian noise. In contrast to existing approaches, the novel method does not make assumptions about the true points on the circle, where the measurements stem from. Closed-form expressions for the measurement update step are derived. Simulations show that the novel method outperforms standard Bayesian approaches for circle fitting
Extended Target Tracking and Classification Using Neural Networks
Extended target/object tracking (ETT) problem involves tracking objects which
potentially generate multiple measurements at a single sensor scan.
State-of-the-art ETT algorithms can efficiently exploit the available
information in these measurements such that they can track the dynamic
behaviour of objects and learn their shapes simultaneously. Once the shape
estimate of an object is formed, it can naturally be utilized by high-level
tasks such as classification of the object type. In this work, we propose to
use a naively deep neural network, which consists of one input, two hidden and
one output layers, to classify dynamic objects regarding their shape estimates.
The proposed method shows superior performance in comparison to a Bayesian
classifier for simulation experiments
Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking
This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g., symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation by means of typical multiple target tracking scenarios
Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference
In this study, we propose a novel extended target tracking algorithm which is
capable of representing the extent of dynamic objects as an ellipsoid with a
time-varying orientation angle. A diagonal positive semi-definite matrix is
defined to model objects' extent within the random matrix framework where the
diagonal elements have inverse-Gamma priors. The resulting measurement equation
is non-linear in the state variables, and it is not possible to find a
closed-form analytical expression for the true posterior because of the absence
of conjugacy. We use the variational Bayes technique to perform approximate
inference, where the Kullback-Leibler divergence between the true and the
approximate posterior is minimized by performing fixed-point iterations. The
update equations are easy to implement, and the algorithm can be used in
real-time tracking applications. We illustrate the performance of the method in
simulations and experiments with real data. The proposed method outperforms the
state-of-the-art methods when compared with respect to accuracy and robustness.Comment: 12 pages, 6 figures, submitted to IEEE TS
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Simultaneous Tracking and Shape Estimation of Extended Objects
This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem, and ii) the lack of statistical knowledge about possible measurement sources on the extended object
Tracking Extended Objects with Active Models and Negative Measurements
Extended object tracking deals with estimating the shape and pose of an object based on noisy point measurements. This task is not straightforward, as we may be faced with scarce low-quality measurements, little a priori information, or we may be unable to observe the entire target. This work aims to address these challenges by incorporating ideas from active contours and exploiting information from negative measurements, which tell us where the target cannot be