An architecture for automatic multimodal video data anonymization to ensure data protection

Ann-Kristin Grosselfinger; David Münch; Michael Arens

doi:10.1117/12.2533031

7 October 2019 An architecture for automatic multimodal video data anonymization to ensure data protection

Ann-Kristin Grosselfinger, David Münch, Michael Arens

Proceedings Volume 11166, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III; 111660Q (2019) https://doi.org/10.1117/12.2533031
Event: SPIE Security + Defence, 2019, Strasbourg, France

Abstract

To perform a data protection concept for our mobile sensor platform (MODISSA), we designed and implemented an anonymization pipeline. This pipeline contains plugins for reading, modifying, and writing different image formats, as well as methods to detect the regions that should be anonymized. This includes a method to determine head positions and an object detector for the license plates, both based on state of the art deep learning methods. These methods are applied for all image sensors on the platform, no matter if they are panoramic RGB, thermal IR, or grayscale cameras. In this paper we focus on the whole face anonymization process. We determine the face region to anonymize on the basis of body pose estimates from OpenPose what proved to lead to robust results. Our anonymization pipeline achieves nearly human performance, with almost no human resources spent. However, to gain perfect anonymization a quick additional human interactive postprocessing step can be performed. We evaluated our pipeline quantitatively and qualitatively on urban example data recorded with MODISSA.

Citation Download Citation

Ann-Kristin Grosselfinger, David Münch, and Michael Arens "An architecture for automatic multimodal video data anonymization to ensure data protection", Proc. SPIE 11166, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III, 111660Q (7 October 2019); https://doi.org/10.1117/12.2533031

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