In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent trans- portation systems (ITS) accurately identify them. Therefore, datasets used to train perception models of ITS must con- tain a significant number of vulnerable road users. How- ever, data protection regulations require that individuals are anonymized in such datasets.

In this work, we introduce a novel deep learning-based pipeline for face anonymization in the context of ITS. In contrast to related methods, we do not use generative adversarial networks (GANs) but build upon recent advances in diffusion models. We propose a two-stage method, which contains a face detection model followed by a latent diffusion model to generate realistic face in-paintings. To demonstrate the versatility of anonymized images, we train segmentation methods on anonymized data and evalu- ate them on non-anonymized data.

Our experiments reveal that our pipeline is better suited to anonymize data for seg- mentation than naive methods and performes comparably with recent GAN-based methods. Moreover, face detectors achieve higher mAP scores for faces anonymized by our method compared to naive or recent GAN-based methods.