Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
This addresses privacy concerns for street-view imagery users by offering an alternative to blurring, though it is an incremental improvement over existing methods.
The paper tackles the problem of privacy protection in street-view panoramas by automatically removing and inpainting moving objects like pedestrians and vehicles, achieving a PSNR of 27.2 dB and L1 loss of 2.5% on a dataset of 1000 images.
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