Visual privacy explained

Visual privacy describes the relationship between collection and dissemination of visual information, the expectation of privacy, and the legal issues surrounding them. These days cameras are ubiquitous. They are found in billons of electronic devices, ranging from smartphones to tablets, and laptops to surveillance cams in homes, business, and public.

Applications

Surveillance

However, privacy and trust implications surrounding it limit its ability to seamlessly blend into the computing environment. It is estimated that over 7 million CCTV cameras were deployed in the UK as of 2022.[1] Camera networks have proliferated across other countries. Tools for controlling how these camera networks are used and modifications to the images and video sent to end-users have been explored.

Homes

At home, visual privacy is involved in protecting private spaces, in shared spaces, and protecting occupants from unwanted outsiders. It may also be a concern between residences without adequate screening.

Technologies enhancing visual privacy

Different technologies can preserve privacy while providing information from surveillance networks. Most of these solutions rely upon the target application to operate in a privacy-preserving manner:[2]

See also

External links

Notes and References

  1. Web site: How Many CCTV Cameras in London? UK CCTV Numbers (Updated 2022) . Clarion UK . 2022-10-04 . 2024-06-18.
  2. Book: Koelle . Marion . Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction . Wolf . Katrin . Boll . Susanne . 2018 . ACM Press . 9781450355681 . Tei '18 . Stockholm, Sweden . 177–187 . Beyond LED Status Lights - Design Requirements of Privacy Notices for Body-worn Cameras . 10.1145/3173225.3173234 . http://dl.acm.org/citation.cfm?doid=3173225.3173234 . 3954480.
  3. Respectful Cameras: Detecting Visual Markers in Real-Time to Address Privacy Concerns. Jeremy Schiff . Marci Meingast. Deirdre K. Mulligan. Shankar Sastry. Ken Goldberg. International Conference on Intelligent Robots and Systems (IROS). San Diego, California. October 2007. 2007.
  4. Web site: Street View revisits Manhattan.
  5. Web site: Eptascape, Inc. MPEG-7 Video Analytics . www.eptascape.com . 13 January 2022 . https://web.archive.org/web/20080621124054/http://www.eptascape.com/ . 21 June 2008 . dead.
  6. Web site: Cardea: Context–Aware Visual Privacy Protection for Photo Taking and Sharing. 2023-12-24 . https://web.archive.org/web/20181108184408/https://home.cse.ust.hk/~jshuaa/papers/mmsys18_cardea.pdf . 2018-11-08.
  7. Book: Pittaluga. Francesco. Koppal. Sanjeev J.. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Privacy preserving optics for miniature vision sensors . June 2015. Boston, MA, USA. IEEE. 314–324. 10.1109/CVPR.2015.7298628. 9781467369640. 10.1.1.944.2193. 14056410.
  8. Book: Hinojosa. Carlos. Niebles. Juan Carlos. Arguello. Henry. Learning Privacy-preserving Optics for Human Pose Estimation . October 2021. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . IEEE/CVF. 2573–2582. 10.1109/ICCV48922.2021.00257 . 978-1-6654-2812-5 .