Adversarial Appearance Learning in Augmented Cityscapes for Pedestrian Recognition in Autonomous Driving

Artem Savkin, Thomas Lapotre, Kevin Strauss, Uzair Akbar, Federico Tombari

Abstract: In the autonomous driving area synthetic data is crucial for cover specific traffic scenarios which autonomous vehicle must handle. This data commonly introduces domain gap between synthetic and real domains. In this paper we deploy data augmentation to generate custom traffic scenarios with VRUs in order to improve pedestrian recognition. We provide a pipeline for augmentation of the Cityscapes dataset with virtual pedestrians. In order to improve augmentation realism of the pipeline we reveal a novel generative network architecture for adversarial learning of the data-set lighting conditions. We also evaluate our approach on the tasks of semantic and instance segmentation.