Toward a Methodology for Training with Synthetic Data on the Example of Pedestrian Detection in a Frame-by-Frame Semantic Segmentation Task

Abstract

In order to make highly/fully automated driving safe, synthetic training and validation data will be required, because critical road situations are too divers and too rare. A few studies on using synthetic data have been published, reporting a general increase in accuracy. In this paper, we propose a novel method to gain more in-depth insights in the quality, performance, and influence of synthetic data during training phase in a bounded setting. We demonstrate this method for the example of pedestrian detection in a frame-by-frame semantic segmentation class.