  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 ensure the subjective quality, 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
