A Scalable and Privacy-Aware IoT Service for Live Video Analytics

被引：66

作者：

Wang, Junjue ^{[1
]}

Amos, Brandon ^{[1
]}

Das, Anupam ^{[1
]}

Pillai, Padmanabhan ^{[2
]}

Sadeh, Norman ^{[1
]}

Satyanarayanan, Mahadev ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

[2] Intel Labs, Hillsboro, OR USA

来源：

PROCEEDINGS OF THE 8TH ACM MULTIMEDIA SYSTEMS CONFERENCE (MMSYS'17) | 2017年

基金：

美国国家科学基金会;

关键词：

Privacy Protection; Privacy Mediator; Face Recognition; Cloudlet; Edge Computing; Cloud Computing; RECOGNITION; EIGENFACES;

D O I：

10.1145/3083187.3083192

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.

引用

页码：38 / 49

页数：12

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