Evaluating visual ADAS components on the COnGRATS dataset
Daniel H. Biedermann, Matthias Ochs, Rudolf Mester · 2016
We present a framework that supports the development and evaluation of vision algorithms in the context of driver assistance applications and traffic surveillance. This framework allows the creation of highly realistic image sequences featuring traffic scenarios. The sequences are created with a realistic state of the art vehicle physics model; different kinds of environments are featured, thus providing a wide range of testing scenarios. Due to the physically-based rendering technique and variable camera models employed for the image rendering process, we can simulate different sensor setups and provide appropriate and fully accurate ground truth data.