3 research outputs found
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 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
Extended Object Tracking and Shape Classification
Recent extended target tracking algorithms provide reliable shape estimates while tracking objects. The estimated extent of the objects can also be used for online classification. In this work, we propose to use a Bayesian classifier to identify different objects based on their contour estimates during tracking. The proposed method uses the uncertainty information provided by the estimation covariance of the tracker