  This paper describes a viewpoint-robust object-based change detection network
(OBJ-CDNet). Mobile cameras such as drive recorders capture images from
different viewpoints each time due to differences in camera trajectory and
shutter timing. However, previous methods for pixel-wise change detection are
vulnerable to the viewpoint differences because they assume aligned image pairs
as inputs. To cope with the difficulty, we introduce a deep graph matching
network that establishes object correspondence between an image pair. The
introduction enables us to detect object-wise scene changes without precise
image alignment. For more accurate object matching, we propose an
epipolar-guided deep graph matching network (EGMNet), which incorporates the
epipolar constraint into the deep graph matching layer used in OBJCDNet. To
evaluate our network's robustness against viewpoint differences, we created
synthetic and real datasets for scene change detection from an image pair. The
experimental results verified the effectiveness of our network.
