用深度学习自动模糊视频中的人脸，保护隐私。
Two Deep Learning Solutions for Automatic Blurring of Faces in Videos
- 用YOLO检测人脸后直接模糊，简单高效。
- 用Unet-like网络端到端生成已模糊人脸的图像。
- 适合需要批量处理监控视频的隐私保护场景。
日常生活中摄像头广泛应用，产生大量可能包含人员、车辆敏感信息（如位置、车牌、外貌特征等）的数据。尤其在公共场所，监控摄像头会记录人脸信息。为保护个人隐私，可对采集的视频应用人脸模糊技术。本文提出两种基于深度学习的解决方案：第一种是直接方法，采用基于YOLO架构的经典目标检测器识别人脸，随后进行模糊处理；第二种是间接方法，训练一个类似Unet的分割网络，直接输出所有人脸已被模糊的输入图像版本。
原文摘要 · Abstract (English)
The widespread use of cameras in everyday life situations generates a vast amount of data that may contain sensitive information about the people and vehicles moving in front of them (location, license plates, physical characteristics, etc). In particular, people's faces are recorded by surveillance cameras in public spaces. In order to ensure the privacy of individuals, face blurring techniques can be applied to the collected videos. In this paper we present two deep-learning based options to tackle the problem. First, a direct approach, consisting of a classical object detector (based on the YOLO architecture) trained to detect faces, which are subsequently blurred. Second, an indirect approach, in which a Unet-like segmentation network is trained to output a version of the input image in which all the faces have been blurred.
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