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Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework

Authors :
Padmanabhan Pillai
Brandon Amos
Anupam Das
Norman Sadeh
Mahadev Satyanarayanan
Junjue Wang
Source :
ACM Transactions on Multimedia Computing, Communications, and Applications. 14:1-24
Publication Year :
2018
Publisher :
Association for Computing Machinery (ACM), 2018.

Abstract

We show how to build the components of a privacy-aware, live video analytics ecosystem from the bottom up, starting with OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with interframe 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 and show how it can be an enabler for a vibrant ecosystem and marketplace of privacy-aware video streams and analytics services.

Details

ISSN :
15516865 and 15516857
Volume :
14
Database :
OpenAIRE
Journal :
ACM Transactions on Multimedia Computing, Communications, and Applications
Accession number :
edsair.doi...........b24e301fe115887fa1f90c976a318479