Privacy protection in street-view panoramas using depth and multi-view imagery

Ries Uittenbogaard, Clint Sebastian, Julien Vijverberg, Bas Boom, Dariu Gavrila, Peter H.N. De With

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

The current paradigm in privacy protection in street- view images is to detect and blur sensitive information. In this paper, we propose a framework that is an alternative to blurring, which automatically removes and inpaints moving objects (e.g. pedestrians, vehicles) in street-view imagery. We propose a novel moving object segmentation algorithm exploiting consistencies in depth across multiple street-view images that are later combined with the results of a segmentation network. The detected moving objects are removed and inpainted with information from other views, to obtain a realistic output image such that the moving object is not visible anymore. We evaluate our results on a dataset of 1000 images to obtain a peak noise-to-signal ratio (PSNR) and L1 loss of 27.2 dB and 2.5%, respectively. To assess overall quality, we also report the results of a survey conducted on 35 professionals, asked to visually inspect the images whether object removal and inpainting had taken place. The inpainting dataset will be made publicly available for scientific benchmarking purposes at https://research.cyclomedia.com/.
Original languageEnglish
Title of host publicationProceedings IEEE Computer Vision and Pattern Recognition (CVPR 2019)
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Number of pages10
ISBN (Print)978-1-7281-3293-8
DOIs
Publication statusPublished - 2019
EventCVPR 2019: IEEE Conference on Computer Vision and Pattern Recognition - Long Beach, United States
Duration: 16 Jun 201920 Jun 2019

Conference

ConferenceCVPR 2019: IEEE Conference on Computer Vision and Pattern Recognition
CountryUnited States
CityLong Beach
Period16/06/1920/06/19

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