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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2409.14828 |
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| _version_ | 1866913513421668352 |
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| author | Plaud, Roman Lisani, Jose-Luis |
| author_facet | Plaud, Roman Lisani, Jose-Luis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two Deep Learning Solutions for Automatic Blurring of Faces in Videos Plaud, Roman Lisani, Jose-Luis Computer Vision and Pattern Recognition 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. |
| title | Two Deep Learning Solutions for Automatic Blurring of Faces in Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.14828 |