  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.
