SoK: Multi-Perspective-Video-Anonymization
Authors: Islam Amar (Karlsruhe Institute of Technology, Germany), Omar Moured (Karlsruhe Institute of Technology, Germany), Simon Hanisch (Karlsruhe Institute of Technology, Germany), Thorsten Strufe (Karlsruhe Institute of Technology, Germany)
Volume: 2026
Issue: 3
Pages: 317–340
DOI: https://doi.org/10.56553/popets-2026-0084
Abstract: Video data has become central in many modern systems, from public surveillance to autonomous vehicles, but its widespread use raises serious concerns about exposing people’s identities and behaviors. This survey takes a structured look at how recent research has tried to address these concerns through video anonymization. We conduct a systematic review of the literature and organize existing work into a taxonomy that separates different anonymization strategies, the visual regions they target, and the assumptions they make about the video setting. By examining these categories, we highlight how current methods approach privacy protection and where they tend to fall short. A consistent pattern across the field is that most studies are designed for single-view videos, even though many real environments involve multiple synchronized cameras. Only a handful of works consider this multi-perspective setting, revealing a clear disconnect between research and real-world needs. We also review the datasets and evaluation metrics commonly used in anonymization studies and show that they rarely capture multi-view complexity, underscoring the need for more representative benchmarks. Finally, we discuss the utility goals that anonymization methods aim to preserve and outline key gaps that future work must address to support practical, multi-camera video applications.
Keywords: Systematization, Privacy, Anonymization, Video
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