Wearable cameras are increasingly used in many different applications from law enforcement to medicine. In this paper1, we consider an application of using a wearable camera to record one-on-one therapy with a child in a classroom or clinic. To protect the privacy of other individuals in the same environment, we introduce a new visual privacy paradigm called privacy bubble. Privacy bubble is a virtual zone centered around the camera for observation whereas the rest of the environment and people are obfuscated. In contrast to most existing visual privacy systems that rely on visual classifier, privacy bubble is based on depth estimation to determine the extent of privacy protection. To demonstrate this concept, we construct a wearable stereo-camera for depth estimation on the Raspberry Pi platform. We also propose a novel framework to quantify the uncertainty in depth measurements so as to minimize a statistical privacy risk in constructing the depth-based privacy bubble. The effectiveness of the proposed scheme is demonstrated with preliminary experimental results.
|Title of host publication||2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016|
|State||Published - Sep 22 2016|
|Event||2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States|
Duration: Jul 11 2016 → Jul 15 2016
|Name||2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016|
|Conference||2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016|
|Period||7/11/16 → 7/15/16|
Bibliographical noteFunding Information:
This work was supported in part by the National Science Foundation under Grant 1237134.
© 2016 IEEE.
- Depth uncertainty
- Privacy bubble
- Privacy protection
- Stereo quantization
- Wearable camera
ASJC Scopus subject areas
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition