Privacy-Protection Drone Patrol System based on Face Anonymization
- URL: http://arxiv.org/abs/2005.14390v1
- Date: Fri, 29 May 2020 05:14:18 GMT
- Title: Privacy-Protection Drone Patrol System based on Face Anonymization
- Authors: Harim Lee, Myeung Un Kim, Yeongjun Kim, Hyeonsu Lyu, and Hyun Jong
Yang
- Abstract summary: This work proposes face-anonymizing drone patrol system.
One person's face in a video is transformed into a different face with facial components maintained.
Our system is evaluated with a customized drone consisting of a high-resolution camera, a companion computer, and a drone control computer.
- Score: 1.20050068684031
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The robot market has been growing significantly and is expected to become 1.5
times larger in 2024 than what it was in 2019. Robots have attracted attention
of security companies thanks to their mobility. These days, for security
robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting
their advantage: they can even go to any hazardous place that humans cannot
access. For UAVs, Drone has been a representative model and has several merits
to consist of various sensors such as high-resolution cameras. Therefore, Drone
is the most suitable as a mobile surveillance robot. These attractive
advantages such as high-resolution cameras and mobility can be a double-edged
sword, i.e., privacy infringement. Surveillance drones take videos with
high-resolution to fulfill their role, however, those contain a lot of privacy
sensitive information. The indiscriminate shooting is a critical issue for
those who are very reluctant to be exposed. To tackle the privacy infringement,
this work proposes face-anonymizing drone patrol system. In this system, one
person's face in a video is transformed into a different face with facial
components maintained. To construct our privacy-preserving system, we have
adopted the latest generative adversarial networks frameworks and have some
modifications on losses of those frameworks. Our face-anonymzing approach is
evaluated with various public face-image and video dataset. Moreover, our
system is evaluated with a customized drone consisting of a high-resolution
camera, a companion computer, and a drone control computer. Finally, we confirm
that our system can protect privacy sensitive information with our
face-anonymzing algorithm while preserving the performance of robot perception,
i.e., simultaneous localization and mapping.
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