mlsanityData to 7 Oct 2026

EgoBlur: Responsible Innovation in Aria

arXiv 2308.13093 ↗First posted 24 Aug 2023cs.CVcited by 12

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Abstract (arXiv)

Project Aria pushes the frontiers of Egocentric AI with large-scale real-world data collection using purposely designed glasses with privacy first approach. To protect the privacy of bystanders being recorded by the glasses, our research protocols are designed to ensure recorded video is processed by an AI anonymization model that removes bystander faces and vehicle license plates. Detected face and license plate regions are processed with a Gaussian blur such that these personal identification information (PII) regions are obscured. This process helps to ensure that anonymized versions of the video is retained for research purposes. In Project Aria, we have developed a state-of-the-art anonymization system EgoBlur. In this paper, we present extensive analysis of EgoBlur on challenging datasets comparing its performance with other state-of-the-art systems from industry and academia including extensive Responsible AI analysis on recently released Casual Conversations V2 dataset.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 24 Aug 2023.

Builds on

8 of 14 references.

References · 8

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2022Exploring Plain Vision Transformer Backbones for Object Detectioncited by 723
  4. 2021Knowledge distillation: A good teacher is patient and consistentcited by 255
  5. 2019BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUscited by 74
  6. 2023LDFA: Latent Diffusion Face Anonymization for Self-driving Applicationscited by 21
  7. 2021CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulationcited by 12
  8. 2021Temporally coherent video anonymization through GAN inpaintingcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 4.

12 papers.