Building intelligent mobile camera systems : visual privacy by design meets social interaction

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 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.

Date of Award	2019
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology