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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通过视频直播中不相关的面部跟踪和像素化保护个人隐私

迄今为止，预期的保护隐私的像素化任务仍然是劳动密集型的，尚待研究。随着视频实时流媒体的盛行，在流媒体期间建立在线面部像素化机制已成为当务之急。在本文中，我们开发了一种新方法，称为视频实时流中的面部像素化（FPVLS），可以在不受限制的流活动期间生成自动的个人隐私过滤。简单地使用多脸跟踪器会遇到目标漂移，计算效率和像素过多的问题。因此，为了快速准确地对无关人员的脸部进行像素化，FPVLS采用两个核心阶段的帧到视频结构进行组织。在单个帧上，FPVLS利用基于图像的面部检测和嵌入网络来生成面部向量。在原始轨迹生成阶段，提出的定位增量亲和力传播（PIAP）聚类算法利用人脸矢量和定位信息来快速将同一个人的脸部跨帧关联。这种逐帧累积的原始轨迹可能是断续的，并且在视频级别上不可靠。因此，我们进一步介绍了轨迹细化阶段，该阶段将提议网络与基于经验似然比（ELR）统计数据的两样本测试合并，以细化原始轨迹。高斯滤波器放置在精炼的轨迹上以进行最终像素化。在我们收集的视频实时流数据集上，FPVLS获得令人满意的准确性，实时效率，并包含过像素化问题。