2 research outputs found
Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
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.comComment: Accepted to CVPR 2019. Dataset (and provided link) will be made
available before the CVP
Person Re-identification in Aerial Imagery
Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles
(UAVs), visual surveillance by utilizing the UAV platform has been very
attractive. Most of the research works for UAV captured visual data are mainly
focused on the tasks of object detection and tracking. However, limited
attention has been paid to the task of person Re-identification (ReID) which
has been widely studied in ordinary surveillance cameras with fixed
emplacements. In this paper, to facilitate the research of person ReID in
aerial imagery, we collect a large scale airborne person ReID dataset named as
Person ReID for Aerial Imagery (PRAI-1581), which consists of 39,461 images of
1581 person identities. The images of the dataset are shot by two DJI consumer
UAVs flying at an altitude ranging from 20 to 60 meters above the ground, which
covers most of the real UAV surveillance scenarios. In addition, we propose to
utilize subspace pooling of convolution feature maps to represent the input
person images. Our method can learn a discriminative and compact feature
representation for ReID in aerial imagery and can be trained in an end-to-end
fashion efficiently. We conduct extensive experiments on the proposed dataset
and the experimental results demonstrate that re-identify persons in aerial
imagery is a challenging problem, where our method performs favorably against
state of the arts. Our dataset can be accessed via
\url{https://github.com/stormyoung/PRAI-1581}.Comment: IEEE Transactions on Multimedi