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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.19173 |
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| _version_ | 1866911912302739456 |
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| author | Triess, Sabrina Cynthia Leitritz, Timo Jauch, Christian |
| author_facet | Triess, Sabrina Cynthia Leitritz, Timo Jauch, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19173 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring AI-based Anonymization of Industrial Image and Video Data in the Context of Feature Preservation Triess, Sabrina Cynthia Leitritz, Timo Jauch, Christian Computer Vision and Pattern Recognition 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. |
| title | Exploring AI-based Anonymization of Industrial Image and Video Data in the Context of Feature Preservation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.19173 |