To date, the privacy-protection intended pixelation tasks are still labor-intensive and yet to be studied. With the prevailing of video live streaming, establishing an online face pixelation mechanism during streaming is an urgency. In this paper, we develop a new method called Face Pixelation in Video Live Streaming (FPVLS) to generate automatic personal privacy filtering during unconstrained streaming activities. Simply applying multi-face trackers will encounter problems in target drifting, computing efficiency, and over-pixelation. Therefore, for fast and accurate pixelation of irrelevant people's faces, FPVLS is organized in a frame-to-video structure of two core stages. On individual frames, FPVLS utilizes image-based face detection and embedding networks to yield face vectors. In the raw trajectories generation stage, the proposed Positioned Incremental Affinity Propagation (PIAP) clustering algorithm leverages face vectors and positioned information to quickly associate the same person's faces across frames. Such frame-wise accumulated raw trajectories are likely to be intermittent and unreliable on video level. Hence, we further introduce the trajectory refinement stage that merges a proposal network with the two-sample test based on the Empirical Likelihood Ratio (ELR) statistic to refine the raw trajectories. A Gaussian filter is laid on the refined trajectories for final pixelation. On the video live streaming dataset we collected, FPVLS obtains satisfying accuracy, real-time efficiency, and contains the over-pixelation problems.

中文翻译：

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通过视频直播中的不相关面孔跟踪和像素化来保护个人隐私


迄今为止，旨在保护隐私的像素化任务仍然是劳动密集型的，有待研究。随着视频直播的盛行，建立直播过程中的在线人脸像素化机制迫在眉睫。在本文中，我们开发了一种称为视频直播中的面部像素化（FPVLS）的新方法，以在不受约束的流媒体活动期间生成自动个人隐私过滤。单纯应用多脸跟踪器会遇到目标漂移、计算效率和过度像素化等问题。因此，为了快速准确地对不相关的人脸进行像素化，FPVLS 被组织为两个核心阶段的帧到视频结构。在各个帧上，FPVLS 利用基于图像的人脸检测和嵌入网络来生成人脸向量。在原始轨迹生成阶段，所提出的定位增量亲和传播（PIAP）聚类算法利用人脸向量和定位信息来跨帧快速关联同一个人的脸部。这种逐帧累积的原始轨迹在视频级别上可能是间歇性的且不可靠。因此，我们进一步引入了轨迹细化阶段，该阶段将提案网络与基于经验似然比（ELR）统计的双样本测试相结合，以细化原始轨迹。高斯滤波器放置在精细化的轨迹上以进行最终的像素化。在我们收集的视频直播数据集上，FPVLS 获得了令人满意的精度、实时效率，并且包含了过度像素化问题。