EgoBlur: Responsible Innovation in Aria
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:YHK4QGLNrecord.jsonopen to challenge →
read the original abstract
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.
This paper has not been read by Pith yet.
Forward citations
Cited by 9 Pith papers
-
The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform
Spectrascapes is the first open-access multi-spectral terrestrial street-view dataset combining RGB, NIR, and thermal imagery from a mobile bike platform in diverse Dutch urban settings.
-
Towards Context-Aware Image Anonymization with Multi-Agent Reasoning
CAIAMAR employs multi-agent reasoning for context-aware PII anonymization in images, cutting re-identification risks by 73% on benchmarks while maintaining high image quality.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere supplies an open mobile app, 200-hour long-form egocentric dataset, and processing pipeline that enables collection of persistent-state egocentric trajectories on commodity hardware for VLA and foun...
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere releases an open mobile app, processing pipeline, and 200-hour dataset for long-horizon egocentric data collection on commodity hardware to support vision-language-action model training.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
An open framework with a free smartphone app, STERA pipeline, and 200-hour dataset enables hour-plus egocentric data collection on commodity hardware and demonstrates utility by lowering VLA action-prediction error af...
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere releases an open-source smartphone app, 200-hour egocentric dataset with persistent tracking, and pipeline to enable long-horizon data collection for VLA and foundation model research on commodity hardware.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere provides an open infrastructure and 200-hour dataset for collecting long-horizon egocentric trajectories on commodity phones to support VLA model training.
-
Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance
LA3D is a new lightweight method for video anonymization that improves privacy protection for crowd anomaly detection while maintaining detection performance better than existing approaches.
-
MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware
MobileEgo Anywhere releases a 200-hour long-form egocentric dataset with persistent state tracking plus the STERA open infrastructure and processing pipeline to convert commodity mobile captures into training-ready fo...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.