Electro-optical synthetic civilian vehicle data domes

This paper will look at using open source tools (Blender, LuxRender, and Python) to generate a large data set to be used to train an object recognition system. The model produces camera position, camera attitude, and synthetic camera data that can be used for exploitation purposes. We focus on electro-optical (EO) visible sensors to simplify the rendering but this work could be extended to use other rendering tools that support different modalities. The key idea of this paper is to provide an architecture to produce synthetic training data which is modular in design and constructed on open-source off-the-shelf software yielding a physics accurate virtual model of the object we want to recognize. For this paper the objects we are focused on are civilian vehicles. This architecture shows how leveraging existing open-source software allows for practical training of Electro-Optical object recognition algorithms.