Abstract
This paper presents a system for protecting the privacy of specific individuals in video recordings. We address the following two problems: automatic people identification with limited labeled data, and human body obscuring with preserved structure and motion information. In order to address the first problem, we propose a new discriminative learning algorithm to improve people identification accuracy using limited training data labeled from the original video and imperfect pairwise constraints labeled from face obscured video data. We employ a robust face detection and tracking algorithm to obscure human faces in the video. Our experiments in a nursing home environment show that the system can obtain a high accuracy of people identification using limited labeled data and noisy pairwise constraints. The study result indicates that human subjects can perform reasonably well in labeling pairwise constraints with the face masked data. For the second problem, we propose a novel method of body obscuring, which removes the appearance information of the people while preserving rich structure and motion information. The proposed approach provides a way to minimize the risk of exposing the identities of the protected people while maximizing the use of the captured data for activity/behavior analysis.
Highlights
In the last few years, significantly more video cameras continue to be deployed in a variety of locations for different purposes, such as video surveillance and human activity/behavior analysis for medical applications
We have described several useful tools for protecting the privacy of specific individuals in surveillance video
The pairwise constraints can be provided by a large group of unauthorized personnel even when they have no prior knowledge of the subjects in the video data
Summary
In the last few years, significantly more video cameras continue to be deployed in a variety of locations for different purposes, such as video surveillance and human activity/behavior analysis for medical applications These systems have posed significant questions about privacy concerns. Labeling data is a very labor-intensive task but many automatic video analysis algorithms and systems rely on a large amount of training data to achieve a reasonable performance This problem becomes even worse when the privacy protection issue is taken into account, because we have only limited personnel who can access the original data. As one of the constraints, the university’s IRB (Institutional Review Board) has required to protect the identities of patients before unauthorized personnel can access the data This means that only authorized personnel (e.g., doctors and nurses) can help to identify those people.
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