Privacy Protection for Social Video via Background Estimation and CRF-Based Videographer's Intention Modeling
Privacy Protection for Social Video via Background Estimation and CRF-Based Videographer's Intention Modeling
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DOI：
10.1587/transinf.2015edp7378
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发表时间：
2016-04
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影响因子：
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通讯作者：
Yuta Nakashima;N. Babaguchi;Jianping Fan
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文献类型：
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作者：
Yuta Nakashima;N. Babaguchi;Jianping Fan
近来，诸如YouTube、Dailymotion和Facebook的社交网络服务（SNS）的普及使得人们能够容易地发布他们用移动的相机拍摄的个人视频。然而，与此同时，这样的普及也带来了一个新的问题：视频隐私。在这样的社交视频中，人们的隐私，即，他们的外表必须受到保护，但天真地掩盖所有人可能会破坏视频内容。为了解决这个问题，我们专注于摄像师的捕捉意图。在社交视频中，一些人通常对视频内容至关重要。他们被摄像师有意捕捉，称为有意捕捉的人（ICP），其他人则是偶然被框入的（nonICP）。包含非ICP出现的视频可能会侵犯他们的隐私。在本文中，我们开发了一个名为BEPS的系统，它采用了一种新的条件随机场（CRF）为基础的ICP检测方法，以及一种新的方法来掩盖非ICP和保留ICP使用背景估计。BEPS减少了在将视频上传到SNS之前手动模糊非ICP外观的负担。与传统系统相比，BEPS的主要优点如下：（i）它保留了视频内容，以及（ii）它不受人员检测失败的影响;人员检测中的误报不会侵犯隐私。我们的实验结果成功地验证了这两个优点。关键词：有意捕捉人，条件随机场，背景估计，隐私保护，社交视频
The recent popularization of social network services (SNSs), such as YouTube, Dailymotion, and Facebook, enables people to easily publish their personal videos taken with mobile cameras. However, at the same time, such popularity has raised a new problem: video privacy. In such social videos, the privacy of people, i.e., their appearances, must be protected, but naively obscuring all people might spoil the video content. To address this problem, we focus on videographers’ capture intentions. In a social video, some persons are usually essential for the video content. They are intentionally captured by the videographers, called intentionally captured persons (ICPs), and the others are accidentally framed-in (nonICPs). Videos containing the appearances of the non-ICPs might violate their privacy. In this paper, we developed a system called BEPS, which adopts a novel conditional random field (CRF)-based method for ICP detection, as well as a novel approach to obscure non-ICPs and preserve ICPs using background estimation. BEPS reduces the burden of manually obscuring the appearances of the non-ICPs before uploading the video to SNSs. Compared with conventional systems, the following are the main advantages of BEPS: (i) it maintains the video content, and (ii) it is immune to the failure of person detection; false positives in person detection do not violate privacy. Our experimental results successfully validated these two advantages. key words: intentionally captured person, conditional random field, background estimation, privacy protection, social video