dc.contributor.author |
Chen, Andrew |
en |
dc.contributor.author |
Biglari-Abhari, Morteza |
en |
dc.contributor.author |
Wang, Kevin I-Kai |
en |
dc.coverage.spatial |
Honolulu, Hawaii, US |
en |
dc.date.accessioned |
2017-09-03T22:45:47Z |
en |
dc.date.issued |
2017-08 |
en |
dc.identifier.issn |
2160-7508 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/35459 |
en |
dc.description.abstract |
The use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computer vision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. This implicitly requires humans to establish trust, to believe that computer vision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible. |
en |
dc.description.uri |
http://vision.soic.indiana.edu/bright-and-dark-workshop-2017/ |
en |
dc.publisher |
IEEE Xplore |
en |
dc.relation.ispartof |
The First International Workshop on The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS 2017), Computer Vision and Pattern Recognition (CVPR) |
en |
dc.relation.ispartofseries |
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Trusting the Computer in Computer Vision: A Privacy-Affirming Framework |
en |
dc.type |
Conference Item |
en |
dc.identifier.doi |
10.1109/CVPRW.2017.178 |
en |
pubs.begin-page |
1360 |
en |
dc.description.version |
AM - Accepted Manuscript |
en |
dc.description.version |
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE Xplore. 1360-1367. Aug 2017 |
en |
dc.rights.holder |
Copyright: IEEE Xplore |
en |
pubs.end-page |
1367 |
en |
pubs.finish-date |
2017-07-21 |
en |
pubs.start-date |
2017-07-21 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |
pubs.subtype |
Proceedings |
en |
pubs.elements-id |
629228 |
en |
pubs.org-id |
Engineering |
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pubs.org-id |
Department of Electrical, Computer and Software Engineering |
en |
dc.identifier.eissn |
2160-7516 |
en |
pubs.record-created-at-source-date |
2017-06-10 |
en |