  The availability of real-world data is a key element for novel developments
in the fields of automotive and traffic research. Aerial imagery has the major
advantage of recording multiple objects simultaneously and overcomes
limitations such as occlusions. However, there are only few data sets
available. This work describes a process to estimate a precise vehicle position
from aerial imagery. A robust object detection is crucial for reliable results,
hence the state-of-the-art deep neural network Mask-RCNN is applied for that
purpose. Two training data sets are employed: The first one is optimized for
detecting the test vehicle, while the second one consists of randomly selected
images recorded on public roads. To reduce errors, several aspects are
accounted for, such as the drone movement and the perspective projection from a
photograph. The estimated position is comapared with a reference system
installed in the test vehicle. It is shown, that a mean accuracy of 20 cm can
be achieved with flight altitudes up to 100 m, Full-HD resolution and a
frame-by-frame detection. A reliable position estimation is the basis for
further data processing, such as obtaining additional vehicle state variables.
The source code, training weights, labeled data and example videos are made
publicly available. This supports researchers to create new traffic data sets
with specific local conditions.
