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Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework
- 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.
- Subjects :
- Multimedia
Computer Networks and Communications
business.industry
Computer science
Inter frame
020206 networking & telecommunications
Cloud computing
02 engineering and technology
computer.software_genre
Frame rate
Facial recognition system
Hardware and Architecture
Analytics
Scalability
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Cloudlet
business
computer
Edge computing
Subjects
Details
- ISSN :
- 15516865 and 15516857
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- ACM Transactions on Multimedia Computing, Communications, and Applications
- Accession number :
- edsair.doi...........b24e301fe115887fa1f90c976a318479