  With rising technologies, the protection of privacy-sensitive information is
becoming increasingly important. In industry and production facilities, image
or video recordings are beneficial for documentation, tracing production errors
or coordinating workflows. Individuals in images or videos need to be
anonymized. However, the anonymized data should be reusable for further
applications. In this work, we apply the Deep Learning-based full-body
anonymization framework DeepPrivacy2, which generates artificial identities, to
industrial image and video data. We compare its performance with conventional
anonymization techniques. Therefore, we consider the quality of identity
generation, temporal consistency, and the applicability of pose estimation and
action recognition.
