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The availability of real-world data is a key element for novel developmentsin the fields of automotive and traffic research. Aerial imagery has the majoradvantage of recording multiple objects simultaneously and overcomeslimitations such as occlusions. However, there are only few data setsavailable. This work describes a process to estimate a precise vehicle positionfrom 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 thatpurpose. Two training data sets are employed: The first one is optimized fordetecting the test vehicle, while the second one consists of randomly selectedimages recorded on public roads. To reduce errors, several aspects areaccounted for, such as the drone movement and the perspective projection from aphotograph. The estimated position is comapared with a reference systeminstalled in the test vehicle. It is shown, that a mean accuracy of 20 cm canbe achieved with flight altitudes up to 100 m, Full-HD resolution and aframe-by-frame detection. A reliable position estimation is the basis forfurther data processing, such as obtaining additional vehicle state variables.The source code, training weights, labeled data and example videos are madepublicly available. This supports researchers to create new traffic data setswith specific local conditions.

Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles