  This paper presents a novel method for detecting scene changes from a pair of
images with a difference of camera viewpoints using a dense optical flow based
change detection network. In the case that camera poses of input images are
fixed or known, such as with surveillance and satellite cameras, the pixel
correspondence between the images captured at different times can be known.
Hence, it is possible to comparatively accurately detect scene changes between
the images by modeling the appearance of the scene. On the other hand, in case
of cameras mounted on a moving object, such as ground and aerial vehicles, we
must consider the spatial correspondence between the images captured at
different times. However, it can be difficult to accurately estimate the camera
pose or 3D model of a scene, owing to the scene changes or lack of imagery. To
solve this problem, we propose a change detection convolutional neural network
utilizing dense optical flow between input images to improve the robustness to
the difference between camera viewpoints. Our evaluation based on the panoramic
change detection dataset shows that the proposed method outperforms
state-of-the-art change detection algorithms.
