THESIS
2019
xiv, 125 pages : illustrations (some color) ; 30 cm
Abstract
In recent years, cameras become ubiquitous in smartphones, smart glasses, IoT devices,
and surveillance systems. By seeing the physical world and capturing beyond what humans
can see explicitly, camera has been playing an important role in building intelligent
systems and applications such as augmented reality to facilitate people’s daily lives, together
with the advances in computer vision and mobile computing. While cameras bring
people great convenience, concerns on visual privacy invasion are raised at the same time.
Due to the ease of taking photos and recording videos, the popularity of online social media
networks, and the possibility of inferring private information from images and videos
using recognition techniques, people’s attitudes towards the increased amount of ca...[
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In recent years, cameras become ubiquitous in smartphones, smart glasses, IoT devices,
and surveillance systems. By seeing the physical world and capturing beyond what humans
can see explicitly, camera has been playing an important role in building intelligent
systems and applications such as augmented reality to facilitate people’s daily lives, together
with the advances in computer vision and mobile computing. While cameras bring
people great convenience, concerns on visual privacy invasion are raised at the same time.
Due to the ease of taking photos and recording videos, the popularity of online social media
networks, and the possibility of inferring private information from images and videos
using recognition techniques, people’s attitudes towards the increased amount of cameras
are not completely positive.
In this thesis, we build intelligent mobile camera systems to enhance social interaction
and bystander visual privacy. We first introduce Talk2Me, a mobile social network framework
that helps users initiate conversations and make friends with others in the proximity.
Users of Talk2Me can share information with nearby users in a Device-to-Device
fashion and view others’ information in an augmented reality way. On the other hand,
to solve visual privacy issues raised from pervasive mobile cameras, we first propose a
visual indicator-based approach for people to control their visual privacy by wearing tags
and showing hand gestures. We also design a beacon-based visual privacy protection solution
that enables recorders to inform nearby bystanders of camera use and receive privacy
preferences form bystanders following a trigger-and-notification protocol. Finally,
we propose Cardea, a context-aware visual privacy protection mechanism that protects
bystander visual privacy in photos according to users’ context-dependent privacy preferences.
We build prototypes on smartphones. The evaluation results demonstrate the
feasibility and effectiveness of our systems.
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