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Enhancing Lifelogging Privacy by Detecting Screens

Published: 07 May 2016 Publication History

Abstract

Low-cost, lightweight wearable cameras let us record (or 'lifelog') our lives from a 'first-person' perspective for purposes ranging from fun to therapy. But they also capture private information that people may not want to be recorded, especially if images are stored in the cloud or visible to other people. For example, recent studies suggest that computer screens may be lifeloggers' single greatest privacy concern, because many people spend a considerable amount of time in front of devices that display private information. In this paper, we investigate using computer vision to automatically detect computer screens in photo lifelogs. We evaluate our approach on an existing in-situ dataset of 36 people who wore cameras for a week, and show that our technique could help manage privacy in the upcoming era of wearable cameras.

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cover image ACM Conferences
CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
May 2016
6108 pages
ISBN:9781450333627
DOI:10.1145/2858036
© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 07 May 2016

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  1. computer vision
  2. convolutional neural networks
  3. deep learning
  4. lifelogging
  5. privacy
  6. wearable cameras

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CHI'16: CHI Conference on Human Factors in Computing Systems
May 7 - 12, 2016
California, San Jose, USA

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CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;
Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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