Related papers: Two Deep Learning Solutions for Automatic Blurring of Faces in Videos

Two Deep Learning Solutions for Automatic Blurring of Faces in Videos

URL: http://arxiv.org/abs/2409.14828v1
Date: Mon, 23 Sep 2024 08:59:44 GMT
Title: Two Deep Learning Solutions for Automatic Blurring of Faces in Videos
Authors: Roman Plaud, Jose-Luis Lisani,
Abstract summary: In this paper we present two deep-learning based options to tackle the problem of face blurring in surveillance videos. First, a direct approach, consisting of a classical object detector 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.
Score: 0.8287206589886879
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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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