With the prevailing of live video streaming, establishing an online pixelation method for privacy-sensitive objects is an urgency. Caused by the inaccurate detection of privacy-sensitive objects, simply migrating the tracking-by-detection structure into the online form will incur problems in target initialization, drifting, and over-pixelation. To cope with the inevitable but impacting detection issue, we propose a novel Privacy-sensitive Objects Pixelation (PsOP) framework for automatic personal privacy filtering during live video streaming. Leveraging pre-trained detection networks, our PsOP is extendable to any potential privacy-sensitive objects pixelation. Employing the embedding networks and the proposed Positioned Incremental Affinity Propagation (PIAP) clustering algorithm as the backbone, our PsOP unifies the pixelation of discriminating and indiscriminating pixelation objects through trajectories generation. In addition to the pixelation accuracy boosting, experiments on the streaming video data we built show that the proposed PsOP can significantly reduce the over-pixelation ratio in privacy-sensitive object pixelation.

中文翻译：

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实时视频流的隐私敏感对象像素化

随着实时视频流的流行，为隐私敏感对象建立在线像素化方法已成为当务之急。由于对隐私敏感对象的检测不准确，仅将按检测跟踪的结构迁移到在线形式将导致目标初始化，漂移和像素过度化的问题。为了解决不可避免但影响深远的检测问题，我们提出了一种新颖的隐私敏感对象像素化（PsOP）框架，用于在实时视频流期间自动进行个人隐私过滤。利用预训练的检测网络，我们的PsOP可扩展到任何潜在的隐私敏感对象像素化。以嵌入网络和拟议的位置增量亲和力传播（PIAP）聚类算法为骨干，我们的PsOP通过轨迹生成来统一区分和不区分像素对象的像素。除了提高像素化精度外，我们对流视频数据进行的实验表明，所提出的PsOP可以显着降低隐私敏感对象像素化中的过像素化率。