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.

翻译：随着技术发展，隐私敏感信息的保护日益重要。在工业和生产设施中，图像或视频记录有利于文档记录、追踪生产错误或协调工作流程。图像或视频中的个体需要进行匿名化处理。然而，匿名化后的数据应能适用于后续应用。本研究将基于深度学习的全身匿名化框架DeepPrivacy2（可生成人工身份）应用于工业图像和视频数据，并将其性能与传统匿名化技术进行比较。为此，我们考察了身份生成质量、时间一致性，以及姿态估计与行为识别任务的适用性。