Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 15, 2018
- Source ID
- 10.1145/3209659
Entities
People
- Anupam Das
- Brandon Amos
- Junjue Wang
- Mahadev Satyanarayanan
- Norman Sadeh
- Padmanabhan Pillai
Organizations
- Carnegie Mellon University
- Defense Advanced Research Projects Agency
- Intel Corporation
- National Science Foundation