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Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

V. Haltakov , C. Unger, S. Ilic
Framework for generation of synthetic ground truth data for driver assistance applications
35th German Conference on Pattern Recognition, 3.-6. September 2013 (bib)

High precision ground truth data is a very important factor for the development and evaluation of computer vision algorithms and especially for advanced driver assistance systems. Unfortunately, some types of data, like accurate optical flow and depth as well as pixel-wise semantic annotations are very difficult to obtain. In order to address this problem, in this paper we present a new framework for the generation of high quality synthetic camera images, depth and optical flow maps and pixel-wise semantic annotations. The framework is based on a realistic driving simulator called VDrift [1], which allows us to create traffic scenarios very similar to those in real life. We show how we can use the proposed framework to generate an extensive dataset for the task of multi-class image segmentation. We use the dataset to train a pairwise CRF model and to analyse the effects of using various combinations of features in different image modalities.
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